id	sid	tid	token	lemma	pos
ajst-20810	1	1	academic	academic	ADJ
ajst-20810	1	2	journal	journal	NOUN
ajst-20810	1	3	of	of	ADP
ajst-20810	1	4	science	science	NOUN
ajst-20810	1	5	and	and	CCONJ
ajst-20810	1	6	technology	technology	NOUN
ajst-20810	1	7	issn	issn	NOUN
ajst-20810	1	8	:	:	PUNCT
ajst-20810	1	9	2771	2771	NUM
ajst-20810	1	10	-	-	SYM
ajst-20810	1	11	3032	3032	NUM
ajst-20810	1	12	|	|	NOUN
ajst-20810	1	13	vol	vol	NOUN
ajst-20810	1	14	.	.	PROPN
ajst-20810	2	1	10	10	NUM
ajst-20810	2	2	,	,	PUNCT
ajst-20810	2	3	no	no	INTJ
ajst-20810	2	4	.	.	NOUN
ajst-20810	2	5	3	3	NUM
ajst-20810	2	6	,	,	PUNCT
ajst-20810	2	7	2024	2024	NUM
ajst-20810	2	8	178	178	NUM
ajst-20810	2	9	review	review	NOUN
ajst-20810	2	10	of	of	ADP
ajst-20810	2	11	convolutional	convolutional	ADJ
ajst-20810	2	12	neural	neural	ADJ
ajst-20810	2	13	network	network	NOUN
ajst-20810	2	14	models	model	NOUN
ajst-20810	2	15	and	and	CCONJ
ajst-20810	2	16	image	image	NOUN
ajst-20810	2	17	classification	classification	NOUN
ajst-20810	2	18	weiqi	weiqi	NOUN
ajst-20810	2	19	hua1	hua1	NOUN
ajst-20810	2	20	,	,	PUNCT
ajst-20810	2	21	chunzhong	chunzhong	NOUN
ajst-20810	2	22	li	li	PROPN
ajst-20810	2	23	1	1	NUM
ajst-20810	2	24	,	,	PUNCT
ajst-20810	2	25	*	*	PUNCT
ajst-20810	2	26	,	,	PUNCT
ajst-20810	2	27	xinsheng	xinsheng	PROPN
ajst-20810	2	28	wang2	wang2	PROPN
ajst-20810	3	1	1	1	NUM
ajst-20810	3	2	college	college	NOUN
ajst-20810	3	3	of	of	ADP
ajst-20810	3	4	statistics	statistic	NOUN
ajst-20810	3	5	and	and	CCONJ
ajst-20810	3	6	applied	applied	ADJ
ajst-20810	3	7	mathematics	mathematic	NOUN
ajst-20810	3	8	,	,	PUNCT
ajst-20810	3	9	anhui	anhui	PROPN
ajst-20810	3	10	university	university	PROPN
ajst-20810	3	11	of	of	ADP
ajst-20810	3	12	finance	finance	NOUN
ajst-20810	3	13	and	and	CCONJ
ajst-20810	3	14	economics	economic	NOUN
ajst-20810	3	15	,	,	PUNCT
ajst-20810	3	16	bengbu	bengbu	NOUN
ajst-20810	3	17	233030	233030	NUM
ajst-20810	3	18	,	,	PUNCT
ajst-20810	3	19	china	china	PROPN
ajst-20810	3	20	2	2	NUM
ajst-20810	3	21	school	school	NOUN
ajst-20810	3	22	of	of	ADP
ajst-20810	3	23	information	information	NOUN
ajst-20810	3	24	engineering	engineering	PROPN
ajst-20810	3	25	,	,	PUNCT
ajst-20810	3	26	jiangxi	jiangxi	PROPN
ajst-20810	3	27	university	university	PROPN
ajst-20810	3	28	of	of	ADP
ajst-20810	3	29	science	science	NOUN
ajst-20810	3	30	and	and	CCONJ
ajst-20810	3	31	technology	technology	NOUN
ajst-20810	3	32	,	,	PUNCT
ajst-20810	3	33	ganzhou	ganzhou	NOUN
ajst-20810	3	34	341000	341000	NUM
ajst-20810	3	35	,	,	PUNCT
ajst-20810	3	36	china	china	PROPN
ajst-20810	3	37	*	*	PUNCT
ajst-20810	3	38	corresponding	correspond	VERB
ajst-20810	3	39	author	author	NOUN
ajst-20810	3	40	:	:	PUNCT
ajst-20810	3	41	chunzhong	chunzhong	PROPN
ajst-20810	3	42	li	li	PROPN
ajst-20810	3	43	(	(	PUNCT
ajst-20810	3	44	email	email	NOUN
ajst-20810	3	45	:	:	PUNCT
ajst-20810	3	46	czhongli@163.com	czhongli@163.com	NOUN
ajst-20810	3	47	)	)	PUNCT
ajst-20810	3	48	abstract	abstract	NOUN
ajst-20810	3	49	:	:	PUNCT
ajst-20810	3	50	with	with	ADP
ajst-20810	3	51	the	the	DET
ajst-20810	3	52	arrival	arrival	NOUN
ajst-20810	3	53	of	of	ADP
ajst-20810	3	54	the	the	DET
ajst-20810	3	55	era	era	NOUN
ajst-20810	3	56	of	of	ADP
ajst-20810	3	57	big	big	ADJ
ajst-20810	3	58	data	datum	NOUN
ajst-20810	3	59	and	and	CCONJ
ajst-20810	3	60	the	the	DET
ajst-20810	3	61	improvement	improvement	NOUN
ajst-20810	3	62	of	of	ADP
ajst-20810	3	63	computing	compute	VERB
ajst-20810	3	64	power	power	NOUN
ajst-20810	3	65	,	,	PUNCT
ajst-20810	3	66	deep	deep	ADJ
ajst-20810	3	67	learning	learning	NOUN
ajst-20810	3	68	has	have	AUX
ajst-20810	3	69	swept	sweep	VERB
ajst-20810	3	70	the	the	DET
ajst-20810	3	71	world	world	NOUN
ajst-20810	3	72	.	.	PUNCT
ajst-20810	4	1	traditional	traditional	ADJ
ajst-20810	4	2	image	image	NOUN
ajst-20810	4	3	classification	classification	NOUN
ajst-20810	4	4	methods	method	NOUN
ajst-20810	4	5	are	be	AUX
ajst-20810	4	6	difficult	difficult	ADJ
ajst-20810	4	7	to	to	PART
ajst-20810	4	8	deal	deal	VERB
ajst-20810	4	9	with	with	ADP
ajst-20810	4	10	the	the	DET
ajst-20810	4	11	huge	huge	ADJ
ajst-20810	4	12	image	image	NOUN
ajst-20810	4	13	data	datum	NOUN
ajst-20810	4	14	,	,	PUNCT
ajst-20810	4	15	and	and	CCONJ
ajst-20810	4	16	can	can	AUX
ajst-20810	4	17	not	not	PART
ajst-20810	4	18	meet	meet	VERB
ajst-20810	4	19	the	the	DET
ajst-20810	4	20	requirements	requirement	NOUN
ajst-20810	4	21	of	of	ADP
ajst-20810	4	22	people	people	NOUN
ajst-20810	4	23	on	on	ADP
ajst-20810	4	24	the	the	DET
ajst-20810	4	25	accuracy	accuracy	NOUN
ajst-20810	4	26	and	and	CCONJ
ajst-20810	4	27	speed	speed	NOUN
ajst-20810	4	28	of	of	ADP
ajst-20810	4	29	image	image	NOUN
ajst-20810	4	30	classification	classification	NOUN
ajst-20810	4	31	,	,	PUNCT
ajst-20810	4	32	the	the	DET
ajst-20810	4	33	image	image	NOUN
ajst-20810	4	34	classification	classification	NOUN
ajst-20810	4	35	method	method	NOUN
ajst-20810	4	36	based	base	VERB
ajst-20810	4	37	on	on	ADP
ajst-20810	4	38	convolutional	convolutional	ADJ
ajst-20810	4	39	neural	neural	ADJ
ajst-20810	4	40	network	network	NOUN
ajst-20810	4	41	breaks	break	VERB
ajst-20810	4	42	through	through	ADP
ajst-20810	4	43	the	the	DET
ajst-20810	4	44	bottleneck	bottleneck	NOUN
ajst-20810	4	45	of	of	ADP
ajst-20810	4	46	the	the	DET
ajst-20810	4	47	traditional	traditional	ADJ
ajst-20810	4	48	image	image	NOUN
ajst-20810	4	49	classification	classification	NOUN
ajst-20810	4	50	method	method	NOUN
ajst-20810	4	51	,	,	PUNCT
ajst-20810	4	52	and	and	CCONJ
ajst-20810	4	53	becomes	become	VERB
ajst-20810	4	54	the	the	DET
ajst-20810	4	55	mainstream	mainstream	ADJ
ajst-20810	4	56	algorithm	algorithm	NOUN
ajst-20810	4	57	of	of	ADP
ajst-20810	4	58	image	image	NOUN
ajst-20810	4	59	classification	classification	NOUN
ajst-20810	4	60	,	,	PUNCT
ajst-20810	4	61	how	how	SCONJ
ajst-20810	4	62	to	to	PART
ajst-20810	4	63	effectively	effectively	ADV
ajst-20810	4	64	use	use	VERB
ajst-20810	4	65	convolutional	convolutional	ADJ
ajst-20810	4	66	neural	neural	ADJ
ajst-20810	4	67	network	network	NOUN
ajst-20810	4	68	to	to	PART
ajst-20810	4	69	classify	classify	VERB
ajst-20810	4	70	images	image	NOUN
ajst-20810	4	71	has	have	AUX
ajst-20810	4	72	become	become	VERB
ajst-20810	4	73	a	a	DET
ajst-20810	4	74	hot	hot	ADJ
ajst-20810	4	75	spot	spot	NOUN
ajst-20810	4	76	of	of	ADP
ajst-20810	4	77	research	research	NOUN
ajst-20810	4	78	in	in	ADP
ajst-20810	4	79	the	the	DET
ajst-20810	4	80	field	field	NOUN
ajst-20810	4	81	of	of	ADP
ajst-20810	4	82	computer	computer	NOUN
ajst-20810	4	83	vision	vision	NOUN
ajst-20810	4	84	at	at	ADP
ajst-20810	4	85	home	home	NOUN
ajst-20810	4	86	and	and	CCONJ
ajst-20810	4	87	abroad	abroad	ADV
ajst-20810	4	88	.	.	PUNCT
ajst-20810	5	1	in	in	ADP
ajst-20810	5	2	this	this	DET
ajst-20810	5	3	paper	paper	NOUN
ajst-20810	5	4	,	,	PUNCT
ajst-20810	5	5	we	we	PRON
ajst-20810	5	6	review	review	VERB
ajst-20810	5	7	the	the	DET
ajst-20810	5	8	research	research	NOUN
ajst-20810	5	9	background	background	NOUN
ajst-20810	5	10	,	,	PUNCT
ajst-20810	5	11	significance	significance	NOUN
ajst-20810	5	12	and	and	CCONJ
ajst-20810	5	13	current	current	ADJ
ajst-20810	5	14	research	research	NOUN
ajst-20810	5	15	status	status	NOUN
ajst-20810	5	16	of	of	ADP
ajst-20810	5	17	convolutional	convolutional	ADJ
ajst-20810	5	18	neural	neural	ADJ
ajst-20810	5	19	network	network	NOUN
ajst-20810	5	20	model	model	NOUN
ajst-20810	5	21	and	and	CCONJ
ajst-20810	5	22	image	image	NOUN
ajst-20810	5	23	classification	classification	NOUN
ajst-20810	5	24	,	,	PUNCT
ajst-20810	5	25	study	study	VERB
ajst-20810	5	26	two	two	NUM
ajst-20810	5	27	image	image	NOUN
ajst-20810	5	28	classification	classification	NOUN
ajst-20810	5	29	methods	method	NOUN
ajst-20810	5	30	based	base	VERB
ajst-20810	5	31	on	on	ADP
ajst-20810	5	32	resnet	resnet	NOUN
ajst-20810	5	33	and	and	CCONJ
ajst-20810	5	34	shufflenet	shufflenet	NOUN
ajst-20810	5	35	,	,	PUNCT
ajst-20810	5	36	and	and	CCONJ
ajst-20810	5	37	provide	provide	VERB
ajst-20810	5	38	a	a	DET
ajst-20810	5	39	comprehensive	comprehensive	ADJ
ajst-20810	5	40	review	review	NOUN
ajst-20810	5	41	of	of	ADP
ajst-20810	5	42	the	the	DET
ajst-20810	5	43	construction	construction	NOUN
ajst-20810	5	44	methods	method	NOUN
ajst-20810	5	45	and	and	CCONJ
ajst-20810	5	46	characteristics	characteristic	NOUN
ajst-20810	5	47	of	of	ADP
ajst-20810	5	48	the	the	DET
ajst-20810	5	49	two	two	NUM
ajst-20810	5	50	deep	deep	ADJ
ajst-20810	5	51	convolutional	convolutional	ADJ
ajst-20810	5	52	neural	neural	ADJ
ajst-20810	5	53	network	network	NOUN
ajst-20810	5	54	model	model	NOUN
ajst-20810	5	55	structures	structure	NOUN
ajst-20810	5	56	,	,	PUNCT
ajst-20810	5	57	and	and	CCONJ
ajst-20810	5	58	finally	finally	ADV
ajst-20810	5	59	compare	compare	VERB
ajst-20810	5	60	and	and	CCONJ
ajst-20810	5	61	analyse	analyse	VERB
ajst-20810	5	62	the	the	DET
ajst-20810	5	63	performance	performance	NOUN
ajst-20810	5	64	of	of	ADP
ajst-20810	5	65	the	the	DET
ajst-20810	5	66	two	two	NUM
ajst-20810	5	67	classification	classification	NOUN
ajst-20810	5	68	models	model	NOUN
ajst-20810	5	69	.	.	PUNCT
ajst-20810	6	1	keywords	keyword	NOUN
ajst-20810	6	2	:	:	PUNCT
ajst-20810	6	3	convolutional	convolutional	ADJ
ajst-20810	6	4	neural	neural	ADJ
ajst-20810	6	5	network	network	NOUN
ajst-20810	6	6	,	,	PUNCT
ajst-20810	6	7	image	image	NOUN
ajst-20810	6	8	classification	classification	NOUN
ajst-20810	6	9	,	,	PUNCT
ajst-20810	6	10	resnet	resnet	NOUN
ajst-20810	6	11	,	,	PUNCT
ajst-20810	6	12	shufflenet	shufflenet	NOUN
ajst-20810	6	13	.	.	PUNCT
ajst-20810	7	1	1	1	X
ajst-20810	7	2	.	.	X
ajst-20810	7	3	introduction	introduction	NOUN
ajst-20810	7	4	image	image	NOUN
ajst-20810	7	5	classification	classification	NOUN
ajst-20810	7	6	is	be	AUX
ajst-20810	7	7	an	an	DET
ajst-20810	7	8	important	important	ADJ
ajst-20810	7	9	research	research	NOUN
ajst-20810	7	10	direction	direction	NOUN
ajst-20810	7	11	in	in	ADP
ajst-20810	7	12	the	the	DET
ajst-20810	7	13	field	field	NOUN
ajst-20810	7	14	of	of	ADP
ajst-20810	7	15	artificial	artificial	ADJ
ajst-20810	7	16	intelligence	intelligence	NOUN
ajst-20810	7	17	and	and	CCONJ
ajst-20810	7	18	an	an	DET
ajst-20810	7	19	important	important	ADJ
ajst-20810	7	20	branch	branch	NOUN
ajst-20810	7	21	in	in	ADP
ajst-20810	7	22	image	image	NOUN
ajst-20810	7	23	processing	processing	NOUN
ajst-20810	7	24	.	.	PUNCT
ajst-20810	8	1	it	it	PRON
ajst-20810	8	2	has	have	AUX
ajst-20810	8	3	long	long	ADV
ajst-20810	8	4	attracted	attract	VERB
ajst-20810	8	5	the	the	DET
ajst-20810	8	6	attention	attention	NOUN
ajst-20810	8	7	of	of	ADP
ajst-20810	8	8	academia	academia	NOUN
ajst-20810	8	9	and	and	CCONJ
ajst-20810	8	10	industry	industry	NOUN
ajst-20810	8	11	,	,	PUNCT
ajst-20810	8	12	and	and	CCONJ
ajst-20810	8	13	many	many	ADJ
ajst-20810	8	14	research	research	NOUN
ajst-20810	8	15	results	result	NOUN
ajst-20810	8	16	have	have	AUX
ajst-20810	8	17	been	be	AUX
ajst-20810	8	18	achieved	achieve	VERB
ajst-20810	8	19	.	.	PUNCT
ajst-20810	9	1	image	image	NOUN
ajst-20810	9	2	classification	classification	NOUN
ajst-20810	9	3	is	be	AUX
ajst-20810	9	4	to	to	PART
ajst-20810	9	5	extract	extract	VERB
ajst-20810	9	6	the	the	DET
ajst-20810	9	7	features	feature	NOUN
ajst-20810	9	8	of	of	ADP
ajst-20810	9	9	the	the	DET
ajst-20810	9	10	image	image	NOUN
ajst-20810	9	11	,	,	PUNCT
ajst-20810	9	12	and	and	CCONJ
ajst-20810	9	13	then	then	ADV
ajst-20810	9	14	according	accord	VERB
ajst-20810	9	15	to	to	ADP
ajst-20810	9	16	the	the	DET
ajst-20810	9	17	category	category	NOUN
ajst-20810	9	18	to	to	PART
ajst-20810	9	19	which	which	PRON
ajst-20810	9	20	the	the	DET
ajst-20810	9	21	features	feature	NOUN
ajst-20810	9	22	belong	belong	VERB
ajst-20810	9	23	to	to	PART
ajst-20810	9	24	be	be	AUX
ajst-20810	9	25	classified	classify	VERB
ajst-20810	9	26	.	.	PUNCT
ajst-20810	10	1	early	early	ADJ
ajst-20810	10	2	image	image	NOUN
ajst-20810	10	3	classification	classification	NOUN
ajst-20810	10	4	is	be	AUX
ajst-20810	10	5	to	to	PART
ajst-20810	10	6	use	use	VERB
ajst-20810	10	7	manual	manual	NOUN
ajst-20810	10	8	to	to	PART
ajst-20810	10	9	mark	mark	VERB
ajst-20810	10	10	,	,	PUNCT
ajst-20810	10	11	with	with	SCONJ
ajst-20810	10	12	the	the	DET
ajst-20810	10	13	eyes	eye	NOUN
ajst-20810	10	14	to	to	PART
ajst-20810	10	15	judge	judge	VERB
ajst-20810	10	16	the	the	DET
ajst-20810	10	17	category	category	NOUN
ajst-20810	10	18	of	of	ADP
ajst-20810	10	19	the	the	DET
ajst-20810	10	20	picture	picture	NOUN
ajst-20810	10	21	.	.	PUNCT
ajst-20810	11	1	as	as	SCONJ
ajst-20810	11	2	you	you	PRON
ajst-20810	11	3	can	can	AUX
ajst-20810	11	4	imagine	imagine	VERB
ajst-20810	11	5	,	,	PUNCT
ajst-20810	11	6	this	this	DET
ajst-20810	11	7	primitive	primitive	ADJ
ajst-20810	11	8	method	method	NOUN
ajst-20810	11	9	is	be	AUX
ajst-20810	11	10	very	very	ADV
ajst-20810	11	11	labour	labour	NOUN
ajst-20810	11	12	-	-	PUNCT
ajst-20810	11	13	intensive	intensive	ADJ
ajst-20810	11	14	and	and	CCONJ
ajst-20810	11	15	inefficient	inefficient	ADJ
ajst-20810	11	16	,	,	PUNCT
ajst-20810	11	17	and	and	CCONJ
ajst-20810	11	18	it	it	PRON
ajst-20810	11	19	is	be	AUX
ajst-20810	11	20	increasingly	increasingly	ADV
ajst-20810	11	21	unable	unable	ADJ
ajst-20810	11	22	to	to	PART
ajst-20810	11	23	meet	meet	VERB
ajst-20810	11	24	the	the	DET
ajst-20810	11	25	requirements	requirement	NOUN
ajst-20810	11	26	of	of	ADP
ajst-20810	11	27	the	the	DET
ajst-20810	11	28	ever	ever	ADV
ajst-20810	11	29	-	-	PUNCT
ajst-20810	11	30	changing	change	VERB
ajst-20810	11	31	new	new	ADJ
ajst-20810	11	32	era	era	NOUN
ajst-20810	11	33	.	.	PUNCT
ajst-20810	12	1	as	as	ADV
ajst-20810	12	2	more	more	ADJ
ajst-20810	12	3	and	and	CCONJ
ajst-20810	12	4	more	more	ADJ
ajst-20810	12	5	types	type	NOUN
ajst-20810	12	6	of	of	ADP
ajst-20810	12	7	images	image	NOUN
ajst-20810	12	8	and	and	CCONJ
ajst-20810	12	9	more	more	ADJ
ajst-20810	12	10	and	and	CCONJ
ajst-20810	12	11	more	more	ADV
ajst-20810	12	12	complex	complex	ADJ
ajst-20810	12	13	structures	structure	NOUN
ajst-20810	12	14	,	,	PUNCT
ajst-20810	12	15	how	how	SCONJ
ajst-20810	12	16	to	to	PART
ajst-20810	12	17	effectively	effectively	ADV
ajst-20810	12	18	manage	manage	VERB
ajst-20810	12	19	images	image	NOUN
ajst-20810	12	20	is	be	AUX
ajst-20810	12	21	a	a	DET
ajst-20810	12	22	very	very	ADV
ajst-20810	12	23	meaningful	meaningful	ADJ
ajst-20810	12	24	topic	topic	NOUN
ajst-20810	12	25	at	at	ADP
ajst-20810	12	26	present	present	NOUN
ajst-20810	12	27	.	.	PUNCT
ajst-20810	13	1	with	with	ADP
ajst-20810	13	2	the	the	DET
ajst-20810	13	3	advent	advent	NOUN
ajst-20810	13	4	of	of	ADP
ajst-20810	13	5	the	the	DET
ajst-20810	13	6	information	information	NOUN
ajst-20810	13	7	age	age	NOUN
ajst-20810	13	8	,	,	PUNCT
ajst-20810	13	9	the	the	DET
ajst-20810	13	10	image	image	NOUN
ajst-20810	13	11	classification	classification	NOUN
ajst-20810	13	12	technology	technology	NOUN
ajst-20810	13	13	develops	develop	VERB
ajst-20810	13	14	as	as	ADP
ajst-20810	13	15	a	a	DET
ajst-20810	13	16	machine	machine	NOUN
ajst-20810	13	17	identifies	identify	VERB
ajst-20810	13	18	and	and	CCONJ
ajst-20810	13	19	classifies	classify	VERB
ajst-20810	13	20	the	the	DET
ajst-20810	13	21	objectively	objectively	ADV
ajst-20810	13	22	existing	exist	VERB
ajst-20810	13	23	things	thing	NOUN
ajst-20810	13	24	by	by	ADP
ajst-20810	13	25	simulating	simulate	VERB
ajst-20810	13	26	the	the	DET
ajst-20810	13	27	human	human	ADJ
ajst-20810	13	28	visual	visual	ADJ
ajst-20810	13	29	system	system	NOUN
ajst-20810	13	30	.	.	PUNCT
ajst-20810	14	1	in	in	ADP
ajst-20810	14	2	the	the	DET
ajst-20810	14	3	past	past	ADJ
ajst-20810	14	4	decade	decade	NOUN
ajst-20810	14	5	or	or	CCONJ
ajst-20810	14	6	so	so	ADV
ajst-20810	14	7	,	,	PUNCT
ajst-20810	14	8	as	as	SCONJ
ajst-20810	14	9	the	the	DET
ajst-20810	14	10	demand	demand	NOUN
ajst-20810	14	11	for	for	ADP
ajst-20810	14	12	image	image	NOUN
ajst-20810	14	13	processing	process	VERB
ajst-20810	14	14	related	relate	VERB
ajst-20810	14	15	technologies	technology	NOUN
ajst-20810	14	16	has	have	AUX
ajst-20810	14	17	been	be	AUX
ajst-20810	14	18	growing	grow	VERB
ajst-20810	14	19	,	,	PUNCT
ajst-20810	14	20	there	there	PRON
ajst-20810	14	21	have	have	AUX
ajst-20810	14	22	been	be	AUX
ajst-20810	14	23	many	many	ADJ
ajst-20810	14	24	significant	significant	ADJ
ajst-20810	14	25	breakthroughs	breakthrough	NOUN
ajst-20810	14	26	in	in	ADP
ajst-20810	14	27	the	the	DET
ajst-20810	14	28	research	research	NOUN
ajst-20810	14	29	of	of	ADP
ajst-20810	14	30	image	image	NOUN
ajst-20810	14	31	classification	classification	NOUN
ajst-20810	14	32	.	.	PUNCT
ajst-20810	15	1	as	as	ADV
ajst-20810	15	2	early	early	ADV
ajst-20810	15	3	as	as	ADP
ajst-20810	15	4	the	the	DET
ajst-20810	15	5	1980s	1980s	NUM
ajst-20810	15	6	,	,	PUNCT
ajst-20810	15	7	legun	legun	NOUN
ajst-20810	15	8	et	et	PROPN
ajst-20810	15	9	al	al	PROPN
ajst-20810	15	10	.	.	PROPN
ajst-20810	15	11	have	have	AUX
ajst-20810	15	12	proposed	propose	VERB
ajst-20810	15	13	the	the	DET
ajst-20810	15	14	use	use	NOUN
ajst-20810	15	15	of	of	ADP
ajst-20810	15	16	convolutional	convolutional	ADJ
ajst-20810	15	17	neural	neural	ADJ
ajst-20810	15	18	networks[1	networks[1	NUM
ajst-20810	15	19	]	]	PUNCT
ajst-20810	15	20	for	for	ADP
ajst-20810	15	21	image	image	NOUN
ajst-20810	15	22	classification	classification	NOUN
ajst-20810	15	23	.	.	PUNCT
ajst-20810	16	1	since	since	SCONJ
ajst-20810	16	2	then	then	ADV
ajst-20810	16	3	there	there	PRON
ajst-20810	16	4	have	have	AUX
ajst-20810	16	5	been	be	AUX
ajst-20810	16	6	many	many	ADJ
ajst-20810	16	7	competitions	competition	NOUN
ajst-20810	16	8	on	on	ADP
ajst-20810	16	9	image	image	NOUN
ajst-20810	16	10	classification	classification	NOUN
ajst-20810	16	11	,	,	PUNCT
ajst-20810	16	12	e.g.	e.g.	ADV
ajst-20810	16	13	,	,	PUNCT
ajst-20810	16	14	between	between	ADP
ajst-20810	16	15	2005	2005	NUM
ajst-20810	16	16	and	and	CCONJ
ajst-20810	16	17	2012	2012	NUM
ajst-20810	16	18	,	,	PUNCT
ajst-20810	16	19	including	include	VERB
ajst-20810	16	20	pattern	pattern	NOUN
ajst-20810	16	21	analysis	analysis	NOUN
ajst-20810	16	22	,	,	PUNCT
ajst-20810	16	23	statistical	statistical	ADJ
ajst-20810	16	24	modelling	modelling	NOUN
ajst-20810	16	25	,	,	PUNCT
ajst-20810	16	26	computational	computational	ADJ
ajst-20810	16	27	learning	learning	NOUN
ajst-20810	16	28	,	,	PUNCT
ajst-20810	16	29	the	the	DET
ajst-20810	16	30	visual	visual	ADJ
ajst-20810	16	31	object	object	NOUN
ajst-20810	16	32	class	class	NOUN
ajst-20810	16	33	(	(	PUNCT
ajst-20810	16	34	pascal	pascal	PROPN
ajst-20810	16	35	voc	voc	NOUN
ajst-20810	16	36	)	)	PUNCT
ajst-20810	16	37	challenge	challenge	NOUN
ajst-20810	16	38	,	,	PUNCT
ajst-20810	16	39	and	and	CCONJ
ajst-20810	16	40	the	the	DET
ajst-20810	16	41	imagenet	imagenet	ADJ
ajst-20810	16	42	large	large	ADJ
ajst-20810	16	43	scale	scale	NOUN
ajst-20810	16	44	visual	visual	ADJ
ajst-20810	16	45	recognition	recognition	NOUN
ajst-20810	16	46	challenge[2	challenge[2	PROPN
ajst-20810	16	47	]	]	PUNCT
ajst-20810	16	48	.	.	PUNCT
ajst-20810	17	1	since	since	SCONJ
ajst-20810	17	2	krizhevsky	krizhevsky	PROPN
ajst-20810	17	3	et	et	PROPN
ajst-20810	17	4	al	al	PROPN
ajst-20810	17	5	.	.	PROPN
ajst-20810	17	6	proposed	propose	VERB
ajst-20810	17	7	a	a	DET
ajst-20810	17	8	pioneering	pioneer	VERB
ajst-20810	17	9	alexnet[3	alexnet[3	NOUN
ajst-20810	17	10	]	]	X
ajst-20810	17	11	convolutional	convolutional	ADJ
ajst-20810	17	12	neural	neural	ADJ
ajst-20810	17	13	network	network	NOUN
ajst-20810	17	14	model	model	NOUN
ajst-20810	17	15	in	in	ADP
ajst-20810	17	16	2012	2012	NUM
ajst-20810	17	17	,	,	PUNCT
ajst-20810	17	18	the	the	DET
ajst-20810	17	19	performance	performance	NOUN
ajst-20810	17	20	of	of	ADP
ajst-20810	17	21	image	image	NOUN
ajst-20810	17	22	recognition	recognition	NOUN
ajst-20810	17	23	has	have	AUX
ajst-20810	17	24	been	be	AUX
ajst-20810	17	25	significantly	significantly	ADV
ajst-20810	17	26	improved	improve	VERB
ajst-20810	17	27	,	,	PUNCT
ajst-20810	17	28	dramatically	dramatically	ADV
ajst-20810	17	29	reducing	reduce	VERB
ajst-20810	17	30	the	the	DET
ajst-20810	17	31	error	error	NOUN
ajst-20810	17	32	rate	rate	NOUN
ajst-20810	17	33	of	of	ADP
ajst-20810	17	34	image	image	NOUN
ajst-20810	17	35	classification	classification	NOUN
ajst-20810	17	36	.	.	PUNCT
ajst-20810	18	1	this	this	PRON
ajst-20810	18	2	was	be	AUX
ajst-20810	18	3	a	a	DET
ajst-20810	18	4	major	major	ADJ
ajst-20810	18	5	advancement	advancement	NOUN
ajst-20810	18	6	in	in	ADP
ajst-20810	18	7	image	image	NOUN
ajst-20810	18	8	classification	classification	NOUN
ajst-20810	18	9	and	and	CCONJ
ajst-20810	18	10	a	a	DET
ajst-20810	18	11	pioneering	pioneer	VERB
ajst-20810	18	12	development	development	NOUN
ajst-20810	18	13	in	in	ADP
ajst-20810	18	14	the	the	DET
ajst-20810	18	15	field	field	NOUN
ajst-20810	18	16	of	of	ADP
ajst-20810	18	17	deep	deep	ADJ
ajst-20810	18	18	learning	learning	NOUN
ajst-20810	18	19	.	.	PUNCT
ajst-20810	19	1	since	since	SCONJ
ajst-20810	19	2	then	then	ADV
ajst-20810	19	3	,	,	PUNCT
ajst-20810	19	4	various	various	ADJ
ajst-20810	19	5	cnn	cnn	PROPN
ajst-20810	19	6	-	-	PUNCT
ajst-20810	19	7	based	base	VERB
ajst-20810	19	8	image	image	NOUN
ajst-20810	19	9	classification	classification	NOUN
ajst-20810	19	10	methods	method	NOUN
ajst-20810	19	11	have	have	AUX
ajst-20810	19	12	continued	continue	VERB
ajst-20810	19	13	to	to	PART
ajst-20810	19	14	appear	appear	VERB
ajst-20810	19	15	.	.	PUNCT
ajst-20810	20	1	to	to	PART
ajst-20810	20	2	name	name	VERB
ajst-20810	20	3	a	a	DET
ajst-20810	20	4	few	few	ADJ
ajst-20810	20	5	,	,	PUNCT
ajst-20810	20	6	lenet[4	lenet[4	PROPN
ajst-20810	20	7	]	]	X
ajst-20810	20	8	,	,	PUNCT
ajst-20810	20	9	googlenet[5	googlenet[5	PROPN
ajst-20810	20	10	]	]	PUNCT
ajst-20810	20	11	,	,	PUNCT
ajst-20810	20	12	vggnet[6	vggnet[6	PROPN
ajst-20810	20	13	]	]	PUNCT
ajst-20810	20	14	and	and	CCONJ
ajst-20810	20	15	many	many	ADJ
ajst-20810	20	16	other	other	ADJ
ajst-20810	20	17	convolutional	convolutional	ADJ
ajst-20810	20	18	neural	neural	ADJ
ajst-20810	20	19	network	network	NOUN
ajst-20810	20	20	models	model	NOUN
ajst-20810	20	21	.	.	PUNCT
ajst-20810	21	1	the	the	DET
ajst-20810	21	2	performance	performance	NOUN
ajst-20810	21	3	of	of	ADP
ajst-20810	21	4	image	image	NOUN
ajst-20810	21	5	classification	classification	NOUN
ajst-20810	21	6	techniques	technique	NOUN
ajst-20810	21	7	continues	continue	VERB
ajst-20810	21	8	to	to	PART
ajst-20810	21	9	improve	improve	VERB
ajst-20810	21	10	,	,	PUNCT
ajst-20810	21	11	and	and	CCONJ
ajst-20810	21	12	some	some	DET
ajst-20810	21	13	methods	method	NOUN
ajst-20810	21	14	have	have	AUX
ajst-20810	21	15	even	even	ADV
ajst-20810	21	16	surpassed	surpass	VERB
ajst-20810	21	17	the	the	DET
ajst-20810	21	18	human	human	ADJ
ajst-20810	21	19	level	level	NOUN
ajst-20810	21	20	.	.	PUNCT
ajst-20810	22	1	with	with	ADP
ajst-20810	22	2	the	the	DET
ajst-20810	22	3	rapid	rapid	ADJ
ajst-20810	22	4	development	development	NOUN
ajst-20810	22	5	of	of	ADP
ajst-20810	22	6	computers	computer	NOUN
ajst-20810	22	7	and	and	CCONJ
ajst-20810	22	8	the	the	DET
ajst-20810	22	9	great	great	ADJ
ajst-20810	22	10	improvement	improvement	NOUN
ajst-20810	22	11	of	of	ADP
ajst-20810	22	12	computing	compute	VERB
ajst-20810	22	13	power	power	NOUN
ajst-20810	22	14	,	,	PUNCT
ajst-20810	22	15	deep	deep	ADJ
ajst-20810	22	16	learning	learning	NOUN
ajst-20810	22	17	has	have	AUX
ajst-20810	22	18	gradually	gradually	ADV
ajst-20810	22	19	stepped	step	VERB
ajst-20810	22	20	into	into	ADP
ajst-20810	22	21	our	our	PRON
ajst-20810	22	22	vision	vision	NOUN
ajst-20810	22	23	.	.	PUNCT
ajst-20810	23	1	in	in	ADP
ajst-20810	23	2	the	the	DET
ajst-20810	23	3	field	field	NOUN
ajst-20810	23	4	of	of	ADP
ajst-20810	23	5	image	image	NOUN
ajst-20810	23	6	classification	classification	NOUN
ajst-20810	23	7	,	,	PUNCT
ajst-20810	23	8	the	the	DET
ajst-20810	23	9	convolutional	convolutional	ADJ
ajst-20810	23	10	neural	neural	ADJ
ajst-20810	23	11	network	network	NOUN
ajst-20810	23	12	in	in	ADP
ajst-20810	23	13	deep	deep	ADJ
ajst-20810	23	14	learning	learning	NOUN
ajst-20810	23	15	can	can	AUX
ajst-20810	23	16	be	be	AUX
ajst-20810	23	17	very	very	ADV
ajst-20810	23	18	useful	useful	ADJ
ajst-20810	23	19	.	.	PUNCT
ajst-20810	24	1	compared	compare	VERB
ajst-20810	24	2	with	with	ADP
ajst-20810	24	3	traditional	traditional	ADJ
ajst-20810	24	4	image	image	NOUN
ajst-20810	24	5	classification	classification	NOUN
ajst-20810	24	6	methods	method	NOUN
ajst-20810	24	7	,	,	PUNCT
ajst-20810	24	8	it	it	PRON
ajst-20810	24	9	no	no	ADV
ajst-20810	24	10	longer	long	ADV
ajst-20810	24	11	needs	need	VERB
ajst-20810	24	12	to	to	PART
ajst-20810	24	13	manually	manually	ADV
ajst-20810	24	14	describe	describe	VERB
ajst-20810	24	15	and	and	CCONJ
ajst-20810	24	16	extract	extract	VERB
ajst-20810	24	17	features	feature	NOUN
ajst-20810	24	18	from	from	ADP
ajst-20810	24	19	the	the	DET
ajst-20810	24	20	target	target	NOUN
ajst-20810	24	21	image	image	NOUN
ajst-20810	24	22	,	,	PUNCT
ajst-20810	24	23	but	but	CCONJ
ajst-20810	24	24	through	through	ADP
ajst-20810	24	25	the	the	DET
ajst-20810	24	26	neural	neural	ADJ
ajst-20810	24	27	network	network	NOUN
ajst-20810	24	28	to	to	PART
ajst-20810	24	29	autonomously	autonomously	ADV
ajst-20810	24	30	learn	learn	VERB
ajst-20810	24	31	the	the	DET
ajst-20810	24	32	features	feature	NOUN
ajst-20810	24	33	from	from	ADP
ajst-20810	24	34	the	the	DET
ajst-20810	24	35	training	training	NOUN
ajst-20810	24	36	samples	sample	NOUN
ajst-20810	24	37	,	,	PUNCT
ajst-20810	24	38	and	and	CCONJ
ajst-20810	24	39	these	these	DET
ajst-20810	24	40	features	feature	NOUN
ajst-20810	24	41	are	be	AUX
ajst-20810	24	42	closely	closely	ADV
ajst-20810	24	43	related	relate	VERB
ajst-20810	24	44	to	to	ADP
ajst-20810	24	45	the	the	DET
ajst-20810	24	46	classifier	classifier	NOUN
ajst-20810	24	47	,	,	PUNCT
ajst-20810	24	48	which	which	PRON
ajst-20810	24	49	is	be	AUX
ajst-20810	24	50	a	a	DET
ajst-20810	24	51	good	good	ADJ
ajst-20810	24	52	solution	solution	NOUN
ajst-20810	24	53	to	to	ADP
ajst-20810	24	54	the	the	DET
ajst-20810	24	55	problem	problem	NOUN
ajst-20810	24	56	of	of	ADP
ajst-20810	24	57	manually	manually	ADV
ajst-20810	24	58	extracting	extract	VERB
ajst-20810	24	59	features	feature	NOUN
ajst-20810	24	60	and	and	CCONJ
ajst-20810	24	61	selecting	select	VERB
ajst-20810	24	62	classifiers[7	classifiers[7	PROPN
ajst-20810	24	63	]	]	PUNCT
ajst-20810	24	64	.	.	PUNCT
ajst-20810	25	1	therefore	therefore	ADV
ajst-20810	25	2	,	,	PUNCT
ajst-20810	25	3	it	it	PRON
ajst-20810	25	4	is	be	AUX
ajst-20810	25	5	of	of	ADP
ajst-20810	25	6	great	great	ADJ
ajst-20810	25	7	significance	significance	NOUN
ajst-20810	25	8	to	to	PART
ajst-20810	25	9	study	study	VERB
ajst-20810	25	10	image	image	NOUN
ajst-20810	25	11	classification	classification	NOUN
ajst-20810	25	12	methods	method	NOUN
ajst-20810	25	13	based	base	VERB
ajst-20810	25	14	on	on	ADP
ajst-20810	25	15	convolutional	convolutional	ADJ
ajst-20810	25	16	neural	neural	ADJ
ajst-20810	25	17	network	network	NOUN
ajst-20810	25	18	models	model	NOUN
ajst-20810	25	19	in	in	ADP
ajst-20810	25	20	various	various	ADJ
ajst-20810	25	21	scene	scene	NOUN
ajst-20810	25	22	applications	application	NOUN
ajst-20810	25	23	.	.	PUNCT
ajst-20810	26	1	therefore	therefore	ADV
ajst-20810	26	2	,	,	PUNCT
ajst-20810	26	3	two	two	NUM
ajst-20810	26	4	image	image	NOUN
ajst-20810	26	5	classification	classification	NOUN
ajst-20810	26	6	methods	method	NOUN
ajst-20810	26	7	based	base	VERB
ajst-20810	26	8	on	on	ADP
ajst-20810	26	9	resnet[8	resnet[8	NUM
ajst-20810	26	10	]	]	PUNCT
ajst-20810	26	11	and	and	CCONJ
ajst-20810	26	12	shufflenet[9	shufflenet[9	PROPN
ajst-20810	26	13	]	]	X
ajst-20810	26	14	will	will	AUX
ajst-20810	26	15	be	be	AUX
ajst-20810	26	16	discussed	discuss	VERB
ajst-20810	26	17	and	and	CCONJ
ajst-20810	26	18	studied	study	VERB
ajst-20810	26	19	in	in	ADP
ajst-20810	26	20	this	this	DET
ajst-20810	26	21	paper	paper	NOUN
ajst-20810	26	22	.	.	PUNCT
ajst-20810	27	1	2	2	X
ajst-20810	27	2	.	.	X
ajst-20810	27	3	relevant	relevant	ADJ
ajst-20810	27	4	theories	theory	NOUN
ajst-20810	27	5	2.1	2.1	NUM
ajst-20810	27	6	.	.	PUNCT
ajst-20810	27	7	basic	basic	ADJ
ajst-20810	27	8	theory	theory	NOUN
ajst-20810	27	9	of	of	ADP
ajst-20810	27	10	image	image	NOUN
ajst-20810	27	11	classification	classification	NOUN
ajst-20810	27	12	image	image	NOUN
ajst-20810	27	13	classification	classification	NOUN
ajst-20810	27	14	involves	involve	VERB
ajst-20810	27	15	extracting	extract	VERB
ajst-20810	27	16	features	feature	NOUN
ajst-20810	27	17	from	from	ADP
ajst-20810	27	18	the	the	DET
ajst-20810	27	19	input	input	NOUN
ajst-20810	27	20	image	image	NOUN
ajst-20810	27	21	and	and	CCONJ
ajst-20810	27	22	categorising	categorise	VERB
ajst-20810	27	23	them	they	PRON
ajst-20810	27	24	.	.	PUNCT
ajst-20810	28	1	in	in	ADP
ajst-20810	28	2	image	image	NOUN
ajst-20810	28	3	classification	classification	NOUN
ajst-20810	28	4	,	,	PUNCT
ajst-20810	28	5	the	the	DET
ajst-20810	28	6	appropriate	appropriate	ADJ
ajst-20810	28	7	dataset	dataset	NOUN
ajst-20810	28	8	is	be	AUX
ajst-20810	28	9	first	first	ADV
ajst-20810	28	10	selected	select	VERB
ajst-20810	28	11	,	,	PUNCT
ajst-20810	28	12	then	then	ADV
ajst-20810	28	13	the	the	DET
ajst-20810	28	14	dataset	dataset	NOUN
ajst-20810	28	15	is	be	AUX
ajst-20810	28	16	preprocessed	preprocesse	VERB
ajst-20810	28	17	to	to	PART
ajst-20810	28	18	eliminate	eliminate	VERB
ajst-20810	28	19	the	the	DET
ajst-20810	28	20	influence	influence	NOUN
ajst-20810	28	21	of	of	ADP
ajst-20810	28	22	other	other	ADJ
ajst-20810	28	23	factors	factor	NOUN
ajst-20810	28	24	on	on	ADP
ajst-20810	28	25	the	the	DET
ajst-20810	28	26	features	feature	NOUN
ajst-20810	28	27	,	,	PUNCT
ajst-20810	28	28	then	then	ADV
ajst-20810	28	29	the	the	DET
ajst-20810	28	30	feature	feature	NOUN
ajst-20810	28	31	information	information	NOUN
ajst-20810	28	32	is	be	AUX
ajst-20810	28	33	extracted	extract	VERB
ajst-20810	28	34	from	from	ADP
ajst-20810	28	35	the	the	DET
ajst-20810	28	36	image	image	NOUN
ajst-20810	28	37	,	,	PUNCT
ajst-20810	28	38	and	and	CCONJ
ajst-20810	28	39	finally	finally	ADV
ajst-20810	28	40	,	,	PUNCT
ajst-20810	28	41	after	after	ADP
ajst-20810	28	42	learning	learn	VERB
ajst-20810	28	43	and	and	CCONJ
ajst-20810	28	44	training	training	NOUN
ajst-20810	28	45	,	,	PUNCT
ajst-20810	28	46	the	the	DET
ajst-20810	28	47	classification	classification	NOUN
ajst-20810	28	48	result	result	NOUN
ajst-20810	28	49	is	be	AUX
ajst-20810	28	50	finally	finally	ADV
ajst-20810	28	51	obtained	obtain	VERB
ajst-20810	28	52	and	and	CCONJ
ajst-20810	28	53	assigned	assign	VERB
ajst-20810	28	54	a	a	DET
ajst-20810	28	55	label	label	NOUN
ajst-20810	28	56	.	.	PUNCT
ajst-20810	29	1	the	the	DET
ajst-20810	29	2	labels	label	NOUN
ajst-20810	29	3	are	be	AUX
ajst-20810	29	4	obtained	obtain	VERB
ajst-20810	29	5	from	from	ADP
ajst-20810	29	6	a	a	DET
ajst-20810	29	7	predefined	predefine	VERB
ajst-20810	29	8	set	set	NOUN
ajst-20810	29	9	of	of	ADP
ajst-20810	29	10	possible	possible	ADJ
ajst-20810	29	11	classifications	classification	NOUN
ajst-20810	29	12	.	.	PUNCT
ajst-20810	30	1	the	the	DET
ajst-20810	30	2	main	main	ADJ
ajst-20810	30	3	processes	process	NOUN
ajst-20810	30	4	of	of	ADP
ajst-20810	30	5	image	image	NOUN
ajst-20810	30	6	classification	classification	NOUN
ajst-20810	30	7	include	include	VERB
ajst-20810	30	8	image	image	NOUN
ajst-20810	30	9	preprocessing	preprocessing	NOUN
ajst-20810	30	10	,	,	PUNCT
ajst-20810	30	11	image	image	NOUN
ajst-20810	30	12	feature	feature	NOUN
ajst-20810	30	13	description	description	NOUN
ajst-20810	30	14	and	and	CCONJ
ajst-20810	30	15	extraction	extraction	NOUN
ajst-20810	30	16	,	,	PUNCT
ajst-20810	30	17	and	and	CCONJ
ajst-20810	30	18	classifier	classifier	NOUN
ajst-20810	30	19	design	design	NOUN
ajst-20810	30	20	.	.	PUNCT
ajst-20810	31	1	preprocessing	preprocessing	NOUN
ajst-20810	31	2	includes	include	VERB
ajst-20810	31	3	operations	operation	NOUN
ajst-20810	31	4	such	such	ADJ
ajst-20810	31	5	as	as	ADP
ajst-20810	31	6	image	image	NOUN
ajst-20810	31	7	filtering	filtering	NOUN
ajst-20810	31	8	and	and	CCONJ
ajst-20810	31	9	size	size	NOUN
ajst-20810	31	10	normalisation	normalisation	NOUN
ajst-20810	31	11	,	,	PUNCT
ajst-20810	31	12	which	which	PRON
ajst-20810	31	13	are	be	AUX
ajst-20810	31	14	designed	design	VERB
ajst-20810	31	15	to	to	PART
ajst-20810	31	16	facilitate	facilitate	VERB
ajst-20810	31	17	the	the	DET
ajst-20810	31	18	subsequent	subsequent	ADJ
ajst-20810	31	19	processing	processing	NOUN
ajst-20810	31	20	of	of	ADP
ajst-20810	31	21	the	the	DET
ajst-20810	31	22	target	target	NOUN
ajst-20810	31	23	image	image	NOUN
ajst-20810	31	24	;	;	PUNCT
ajst-20810	31	25	image	image	NOUN
ajst-20810	31	26	features	feature	NOUN
ajst-20810	31	27	are	be	AUX
ajst-20810	31	28	descriptions	description	NOUN
ajst-20810	31	29	of	of	ADP
ajst-20810	31	30	salient	salient	NOUN
ajst-20810	31	31	features	feature	NOUN
ajst-20810	31	32	or	or	CCONJ
ajst-20810	31	33	attributes	attribute	NOUN
ajst-20810	31	34	,	,	PUNCT
ajst-20810	31	35	and	and	CCONJ
ajst-20810	31	36	each	each	DET
ajst-20810	31	37	image	image	NOUN
ajst-20810	31	38	has	have	VERB
ajst-20810	31	39	some	some	PRON
ajst-20810	31	40	of	of	ADP
ajst-20810	31	41	its	its	PRON
ajst-20810	31	42	own	own	ADJ
ajst-20810	31	43	179	179	NUM
ajst-20810	31	44	features	feature	NOUN
ajst-20810	31	45	,	,	PUNCT
ajst-20810	31	46	feature	feature	NOUN
ajst-20810	31	47	extraction	extraction	NOUN
ajst-20810	31	48	,	,	PUNCT
ajst-20810	31	49	i.e.	i.e.	X
ajst-20810	31	50	,	,	PUNCT
ajst-20810	31	51	according	accord	VERB
ajst-20810	31	52	to	to	ADP
ajst-20810	31	53	the	the	DET
ajst-20810	31	54	features	feature	NOUN
ajst-20810	31	55	of	of	ADP
ajst-20810	31	56	the	the	DET
ajst-20810	31	57	image	image	NOUN
ajst-20810	31	58	itself	itself	PRON
ajst-20810	31	59	,	,	PUNCT
ajst-20810	31	60	select	select	VERB
ajst-20810	31	61	suitable	suitable	ADJ
ajst-20810	31	62	features	feature	NOUN
ajst-20810	31	63	and	and	CCONJ
ajst-20810	31	64	extract	extract	VERB
ajst-20810	31	65	them	they	PRON
ajst-20810	31	66	efficiently	efficiently	ADV
ajst-20810	31	67	in	in	ADP
ajst-20810	31	68	accordance	accordance	NOUN
ajst-20810	31	69	with	with	ADP
ajst-20810	31	70	a	a	DET
ajst-20810	31	71	certain	certain	ADJ
ajst-20810	31	72	established	establish	VERB
ajst-20810	31	73	way	way	NOUN
ajst-20810	31	74	of	of	ADP
ajst-20810	31	75	classifying	classify	VERB
ajst-20810	31	76	images	image	NOUN
ajst-20810	31	77	;	;	PUNCT
ajst-20810	31	78	a	a	DET
ajst-20810	31	79	classifier	classifier	NOUN
ajst-20810	31	80	is	be	AUX
ajst-20810	31	81	an	an	DET
ajst-20810	31	82	algorithm	algorithm	NOUN
ajst-20810	31	83	for	for	ADP
ajst-20810	31	84	classifying	classify	VERB
ajst-20810	31	85	the	the	DET
ajst-20810	31	86	target	target	NOUN
ajst-20810	31	87	image	image	NOUN
ajst-20810	31	88	according	accord	VERB
ajst-20810	31	89	to	to	ADP
ajst-20810	31	90	the	the	DET
ajst-20810	31	91	selected	select	VERB
ajst-20810	31	92	features	feature	NOUN
ajst-20810	31	93	.	.	PUNCT
ajst-20810	32	1	classifier	classifier	NOUN
ajst-20810	32	2	is	be	AUX
ajst-20810	32	3	an	an	DET
ajst-20810	32	4	algorithm	algorithm	NOUN
ajst-20810	32	5	that	that	PRON
ajst-20810	32	6	classifies	classify	VERB
ajst-20810	32	7	the	the	DET
ajst-20810	32	8	target	target	NOUN
ajst-20810	32	9	image	image	NOUN
ajst-20810	32	10	according	accord	VERB
ajst-20810	32	11	to	to	ADP
ajst-20810	32	12	the	the	DET
ajst-20810	32	13	selected	select	VERB
ajst-20810	32	14	features	feature	NOUN
ajst-20810	32	15	.	.	PUNCT
ajst-20810	33	1	traditional	traditional	ADJ
ajst-20810	33	2	image	image	NOUN
ajst-20810	33	3	classification	classification	NOUN
ajst-20810	33	4	techniques	technique	NOUN
ajst-20810	33	5	mainly	mainly	ADV
ajst-20810	33	6	consist	consist	VERB
ajst-20810	33	7	of	of	ADP
ajst-20810	33	8	two	two	NUM
ajst-20810	33	9	parts	part	NOUN
ajst-20810	33	10	:	:	PUNCT
ajst-20810	33	11	feature	feature	NOUN
ajst-20810	33	12	extraction	extraction	NOUN
ajst-20810	33	13	and	and	CCONJ
ajst-20810	33	14	classifier	classifier	NOUN
ajst-20810	33	15	learning	learning	PROPN
ajst-20810	33	16	.	.	PUNCT
ajst-20810	34	1	feature	feature	NOUN
ajst-20810	34	2	extraction	extraction	NOUN
ajst-20810	34	3	is	be	AUX
ajst-20810	34	4	more	more	ADV
ajst-20810	34	5	important	important	ADJ
ajst-20810	34	6	than	than	ADP
ajst-20810	34	7	classifier	classifier	NOUN
ajst-20810	34	8	learning	learning	NOUN
ajst-20810	34	9	in	in	ADP
ajst-20810	34	10	traditional	traditional	ADJ
ajst-20810	34	11	image	image	NOUN
ajst-20810	34	12	classification	classification	NOUN
ajst-20810	34	13	,	,	PUNCT
ajst-20810	34	14	because	because	SCONJ
ajst-20810	34	15	when	when	SCONJ
ajst-20810	34	16	feature	feature	NOUN
ajst-20810	34	17	extraction	extraction	NOUN
ajst-20810	34	18	does	do	AUX
ajst-20810	34	19	not	not	PART
ajst-20810	34	20	extract	extract	VERB
ajst-20810	34	21	enough	enough	ADJ
ajst-20810	34	22	feature	feature	NOUN
ajst-20810	34	23	information	information	NOUN
ajst-20810	34	24	,	,	PUNCT
ajst-20810	34	25	the	the	DET
ajst-20810	34	26	classification	classification	NOUN
ajst-20810	34	27	accuracy	accuracy	NOUN
ajst-20810	34	28	of	of	ADP
ajst-20810	34	29	the	the	DET
ajst-20810	34	30	classifier	classifier	NOUN
ajst-20810	34	31	will	will	AUX
ajst-20810	34	32	decrease	decrease	VERB
ajst-20810	34	33	and	and	CCONJ
ajst-20810	34	34	the	the	DET
ajst-20810	34	35	error	error	NOUN
ajst-20810	34	36	rate	rate	NOUN
ajst-20810	34	37	of	of	ADP
ajst-20810	34	38	image	image	NOUN
ajst-20810	34	39	classification	classification	NOUN
ajst-20810	34	40	will	will	AUX
ajst-20810	34	41	become	become	VERB
ajst-20810	34	42	higher	high	ADJ
ajst-20810	34	43	.	.	PUNCT
ajst-20810	35	1	among	among	ADP
ajst-20810	35	2	feature	feature	NOUN
ajst-20810	35	3	extraction	extraction	NOUN
ajst-20810	35	4	,	,	PUNCT
ajst-20810	35	5	feature	feature	NOUN
ajst-20810	35	6	coding	code	VERB
ajst-20810	35	7	is	be	AUX
ajst-20810	35	8	one	one	NUM
ajst-20810	35	9	of	of	ADP
ajst-20810	35	10	the	the	DET
ajst-20810	35	11	most	most	ADV
ajst-20810	35	12	studied	study	VERB
ajst-20810	35	13	areas	area	NOUN
ajst-20810	35	14	.	.	PUNCT
ajst-20810	36	1	in	in	ADP
ajst-20810	36	2	feature	feature	NOUN
ajst-20810	36	3	-	-	PUNCT
ajst-20810	36	4	based	base	VERB
ajst-20810	36	5	image	image	NOUN
ajst-20810	36	6	classification	classification	NOUN
ajst-20810	36	7	,	,	PUNCT
ajst-20810	36	8	it	it	PRON
ajst-20810	36	9	can	can	AUX
ajst-20810	36	10	usually	usually	ADV
ajst-20810	36	11	be	be	AUX
ajst-20810	36	12	divided	divide	VERB
ajst-20810	36	13	into	into	ADP
ajst-20810	36	14	three	three	NUM
ajst-20810	36	15	steps	step	NOUN
ajst-20810	36	16	,	,	PUNCT
ajst-20810	36	17	the	the	DET
ajst-20810	36	18	first	first	ADJ
ajst-20810	36	19	step	step	NOUN
ajst-20810	36	20	is	be	AUX
ajst-20810	36	21	the	the	DET
ajst-20810	36	22	input	input	NOUN
ajst-20810	36	23	image	image	NOUN
ajst-20810	36	24	,	,	PUNCT
ajst-20810	36	25	the	the	DET
ajst-20810	36	26	second	second	ADJ
ajst-20810	36	27	step	step	NOUN
ajst-20810	36	28	is	be	AUX
ajst-20810	36	29	feature	feature	NOUN
ajst-20810	36	30	extraction	extraction	NOUN
ajst-20810	36	31	,	,	PUNCT
ajst-20810	36	32	which	which	PRON
ajst-20810	36	33	processes	process	VERB
ajst-20810	36	34	the	the	DET
ajst-20810	36	35	input	input	NOUN
ajst-20810	36	36	image	image	NOUN
ajst-20810	36	37	,	,	PUNCT
ajst-20810	36	38	and	and	CCONJ
ajst-20810	36	39	finally	finally	ADV
ajst-20810	36	40	outputs	output	VERB
ajst-20810	36	41	the	the	DET
ajst-20810	36	42	result	result	NOUN
ajst-20810	36	43	for	for	ADP
ajst-20810	36	44	classification	classification	NOUN
ajst-20810	36	45	.	.	PUNCT
ajst-20810	37	1	this	this	DET
ajst-20810	37	2	part	part	NOUN
ajst-20810	37	3	of	of	ADP
ajst-20810	37	4	feature	feature	NOUN
ajst-20810	37	5	extraction	extraction	NOUN
ajst-20810	37	6	can	can	AUX
ajst-20810	37	7	be	be	AUX
ajst-20810	37	8	further	far	ADV
ajst-20810	37	9	divided	divide	VERB
ajst-20810	37	10	in	in	ADP
ajst-20810	37	11	detail	detail	NOUN
ajst-20810	37	12	into	into	ADP
ajst-20810	37	13	three	three	NUM
ajst-20810	37	14	stages	stage	NOUN
ajst-20810	37	15	:	:	PUNCT
ajst-20810	37	16	descriptor	descriptor	NOUN
ajst-20810	37	17	extraction	extraction	NOUN
ajst-20810	37	18	,	,	PUNCT
ajst-20810	37	19	feature	feature	NOUN
ajst-20810	37	20	coding	coding	NOUN
ajst-20810	37	21	and	and	CCONJ
ajst-20810	37	22	spatial	spatial	ADJ
ajst-20810	37	23	pooling	pooling	NOUN
ajst-20810	37	24	.	.	PUNCT
ajst-20810	38	1	with	with	ADP
ajst-20810	38	2	the	the	DET
ajst-20810	38	3	rapid	rapid	ADJ
ajst-20810	38	4	development	development	NOUN
ajst-20810	38	5	of	of	ADP
ajst-20810	38	6	computers	computer	NOUN
ajst-20810	38	7	and	and	CCONJ
ajst-20810	38	8	the	the	DET
ajst-20810	38	9	great	great	ADJ
ajst-20810	38	10	improvement	improvement	NOUN
ajst-20810	38	11	of	of	ADP
ajst-20810	38	12	computing	compute	VERB
ajst-20810	38	13	power	power	NOUN
ajst-20810	38	14	,	,	PUNCT
ajst-20810	38	15	deep	deep	ADJ
ajst-20810	38	16	learning	learning	NOUN
ajst-20810	38	17	has	have	AUX
ajst-20810	38	18	gradually	gradually	ADV
ajst-20810	38	19	stepped	step	VERB
ajst-20810	38	20	into	into	ADP
ajst-20810	38	21	our	our	PRON
ajst-20810	38	22	vision	vision	NOUN
ajst-20810	38	23	.	.	PUNCT
ajst-20810	39	1	in	in	ADP
ajst-20810	39	2	the	the	DET
ajst-20810	39	3	field	field	NOUN
ajst-20810	39	4	of	of	ADP
ajst-20810	39	5	image	image	NOUN
ajst-20810	39	6	classification	classification	NOUN
ajst-20810	39	7	,	,	PUNCT
ajst-20810	39	8	convolutional	convolutional	ADJ
ajst-20810	39	9	neural	neural	ADJ
ajst-20810	39	10	network	network	NOUN
ajst-20810	39	11	in	in	ADP
ajst-20810	39	12	deep	deep	ADJ
ajst-20810	39	13	learning	learning	NOUN
ajst-20810	39	14	can	can	AUX
ajst-20810	39	15	be	be	AUX
ajst-20810	39	16	very	very	ADV
ajst-20810	39	17	useful	useful	ADJ
ajst-20810	39	18	.	.	PUNCT
ajst-20810	40	1	compared	compare	VERB
ajst-20810	40	2	with	with	ADP
ajst-20810	40	3	traditional	traditional	ADJ
ajst-20810	40	4	image	image	NOUN
ajst-20810	40	5	classification	classification	NOUN
ajst-20810	40	6	methods	method	NOUN
ajst-20810	40	7	,	,	PUNCT
ajst-20810	40	8	cnn	cnn	PROPN
ajst-20810	40	9	-	-	PUNCT
ajst-20810	40	10	based	base	VERB
ajst-20810	40	11	image	image	NOUN
ajst-20810	40	12	classification	classification	NOUN
ajst-20810	40	13	can	can	AUX
ajst-20810	40	14	automatically	automatically	ADV
ajst-20810	40	15	learn	learn	VERB
ajst-20810	40	16	to	to	PART
ajst-20810	40	17	extract	extract	VERB
ajst-20810	40	18	image	image	NOUN
ajst-20810	40	19	features	feature	NOUN
ajst-20810	40	20	,	,	PUNCT
ajst-20810	40	21	has	have	VERB
ajst-20810	40	22	a	a	DET
ajst-20810	40	23	strong	strong	ADJ
ajst-20810	40	24	feature	feature	NOUN
ajst-20810	40	25	expression	expression	NOUN
ajst-20810	40	26	ability	ability	NOUN
ajst-20810	40	27	,	,	PUNCT
ajst-20810	40	28	and	and	CCONJ
ajst-20810	40	29	is	be	AUX
ajst-20810	40	30	an	an	DET
ajst-20810	40	31	important	important	ADJ
ajst-20810	40	32	component	component	NOUN
ajst-20810	40	33	of	of	ADP
ajst-20810	40	34	today	today	NOUN
ajst-20810	40	35	's	's	PART
ajst-20810	40	36	image	image	NOUN
ajst-20810	40	37	classification	classification	NOUN
ajst-20810	40	38	tasks	task	NOUN
ajst-20810	40	39	.	.	PUNCT
ajst-20810	41	1	its	its	PRON
ajst-20810	41	2	classification	classification	NOUN
ajst-20810	41	3	process	process	NOUN
ajst-20810	41	4	consists	consist	VERB
ajst-20810	41	5	of	of	ADP
ajst-20810	41	6	preprocessing	preprocessing	NOUN
ajst-20810	41	7	,	,	PUNCT
ajst-20810	41	8	feature	feature	NOUN
ajst-20810	41	9	extraction	extraction	NOUN
ajst-20810	41	10	,	,	PUNCT
ajst-20810	41	11	learning	learning	NOUN
ajst-20810	41	12	and	and	CCONJ
ajst-20810	41	13	training	training	NOUN
ajst-20810	41	14	.	.	PUNCT
ajst-20810	42	1	among	among	ADP
ajst-20810	42	2	them	they	PRON
ajst-20810	42	3	,	,	PUNCT
ajst-20810	42	4	its	its	PRON
ajst-20810	42	5	focus	focus	NOUN
ajst-20810	42	6	part	part	NOUN
ajst-20810	42	7	is	be	AUX
ajst-20810	42	8	the	the	DET
ajst-20810	42	9	training	training	NOUN
ajst-20810	42	10	process	process	NOUN
ajst-20810	42	11	,	,	PUNCT
ajst-20810	42	12	which	which	PRON
ajst-20810	42	13	can	can	AUX
ajst-20810	42	14	be	be	AUX
ajst-20810	42	15	divided	divide	VERB
ajst-20810	42	16	into	into	ADP
ajst-20810	42	17	forward	forward	ADJ
ajst-20810	42	18	and	and	CCONJ
ajst-20810	42	19	back	back	ADJ
ajst-20810	42	20	propagation	propagation	NOUN
ajst-20810	42	21	.	.	PUNCT
ajst-20810	43	1	when	when	SCONJ
ajst-20810	43	2	training	train	VERB
ajst-20810	43	3	an	an	DET
ajst-20810	43	4	image	image	NOUN
ajst-20810	43	5	,	,	PUNCT
ajst-20810	43	6	the	the	DET
ajst-20810	43	7	results	result	NOUN
ajst-20810	43	8	obtained	obtain	VERB
ajst-20810	43	9	from	from	ADP
ajst-20810	43	10	the	the	DET
ajst-20810	43	11	input	input	NOUN
ajst-20810	43	12	image	image	NOUN
ajst-20810	43	13	after	after	ADP
ajst-20810	43	14	convolutional	convolutional	ADJ
ajst-20810	43	15	layer	layer	NOUN
ajst-20810	43	16	,	,	PUNCT
ajst-20810	43	17	pooling	pool	VERB
ajst-20810	43	18	layer	layer	NOUN
ajst-20810	43	19	,	,	PUNCT
ajst-20810	43	20	and	and	CCONJ
ajst-20810	43	21	classifier	classifier	NOUN
ajst-20810	43	22	are	be	AUX
ajst-20810	43	23	compared	compare	VERB
ajst-20810	43	24	with	with	ADP
ajst-20810	43	25	the	the	DET
ajst-20810	43	26	target	target	NOUN
ajst-20810	43	27	value	value	NOUN
ajst-20810	43	28	,	,	PUNCT
ajst-20810	43	29	after	after	ADP
ajst-20810	43	30	which	which	PRON
ajst-20810	43	31	forward	forward	ADJ
ajst-20810	43	32	propagation	propagation	NOUN
ajst-20810	43	33	or	or	CCONJ
ajst-20810	43	34	back	back	NOUN
ajst-20810	43	35	propagation	propagation	NOUN
ajst-20810	43	36	is	be	AUX
ajst-20810	43	37	chosen	choose	VERB
ajst-20810	43	38	and	and	CCONJ
ajst-20810	43	39	finally	finally	ADV
ajst-20810	43	40	the	the	DET
ajst-20810	43	41	output	output	NOUN
ajst-20810	43	42	is	be	AUX
ajst-20810	43	43	obtained	obtain	VERB
ajst-20810	43	44	.	.	PUNCT
ajst-20810	44	1	2.2	2.2	NUM
ajst-20810	44	2	.	.	PUNCT
ajst-20810	44	3	basic	basic	ADJ
ajst-20810	44	4	theory	theory	NOUN
ajst-20810	44	5	of	of	ADP
ajst-20810	44	6	convolutional	convolutional	ADJ
ajst-20810	44	7	neural	neural	ADJ
ajst-20810	44	8	networks	network	NOUN
ajst-20810	44	9	convolutional	convolutional	ADJ
ajst-20810	44	10	neural	neural	ADJ
ajst-20810	44	11	networks	network	NOUN
ajst-20810	44	12	are	be	AUX
ajst-20810	44	13	similar	similar	ADJ
ajst-20810	44	14	to	to	ADP
ajst-20810	44	15	traditional	traditional	ADJ
ajst-20810	44	16	artificial	artificial	ADJ
ajst-20810	44	17	neural	neural	ADJ
ajst-20810	44	18	networks	network	NOUN
ajst-20810	44	19	in	in	SCONJ
ajst-20810	44	20	that	that	SCONJ
ajst-20810	44	21	they	they	PRON
ajst-20810	44	22	are	be	AUX
ajst-20810	44	23	made	make	VERB
ajst-20810	44	24	up	up	ADP
ajst-20810	44	25	of	of	ADP
ajst-20810	44	26	a	a	DET
ajst-20810	44	27	number	number	NOUN
ajst-20810	44	28	of	of	ADP
ajst-20810	44	29	neurons	neuron	NOUN
ajst-20810	44	30	which	which	PRON
ajst-20810	44	31	are	be	AUX
ajst-20810	44	32	self	self	NOUN
ajst-20810	44	33	-	-	PUNCT
ajst-20810	44	34	optimising	optimise	VERB
ajst-20810	44	35	and	and	CCONJ
ajst-20810	44	36	constantly	constantly	ADV
ajst-20810	44	37	learning	learn	VERB
ajst-20810	44	38	.	.	PUNCT
ajst-20810	45	1	each	each	DET
ajst-20810	45	2	neuron	neuron	NOUN
ajst-20810	45	3	is	be	AUX
ajst-20810	45	4	first	first	ADV
ajst-20810	45	5	assigned	assign	VERB
ajst-20810	45	6	a	a	DET
ajst-20810	45	7	feature	feature	NOUN
ajst-20810	45	8	of	of	ADP
ajst-20810	45	9	the	the	DET
ajst-20810	45	10	input	input	NOUN
ajst-20810	45	11	data	datum	NOUN
ajst-20810	45	12	and	and	CCONJ
ajst-20810	45	13	then	then	ADV
ajst-20810	45	14	proceeds	proceed	VERB
ajst-20810	45	15	to	to	ADP
ajst-20810	45	16	the	the	DET
ajst-20810	45	17	next	next	ADJ
ajst-20810	45	18	operation	operation	NOUN
ajst-20810	45	19	,	,	PUNCT
ajst-20810	45	20	and	and	CCONJ
ajst-20810	45	21	countless	countless	ADJ
ajst-20810	45	22	neurons	neuron	NOUN
ajst-20810	45	23	form	form	VERB
ajst-20810	45	24	together	together	ADV
ajst-20810	45	25	to	to	PART
ajst-20810	45	26	form	form	VERB
ajst-20810	45	27	the	the	DET
ajst-20810	45	28	basis	basis	NOUN
ajst-20810	45	29	of	of	ADP
ajst-20810	45	30	the	the	DET
ajst-20810	45	31	neural	neural	ADJ
ajst-20810	45	32	network	network	NOUN
ajst-20810	45	33	.	.	PUNCT
ajst-20810	46	1	neurons	neuron	NOUN
ajst-20810	46	2	in	in	ADP
ajst-20810	46	3	a	a	DET
ajst-20810	46	4	cnn	cnn	NOUN
ajst-20810	46	5	are	be	AUX
ajst-20810	46	6	typically	typically	ADV
ajst-20810	46	7	composed	compose	VERB
ajst-20810	46	8	of	of	ADP
ajst-20810	46	9	three	three	NUM
ajst-20810	46	10	dimensions	dimension	NOUN
ajst-20810	46	11	,	,	PUNCT
ajst-20810	46	12	input	input	NOUN
ajst-20810	46	13	height	height	NOUN
ajst-20810	46	14	,	,	PUNCT
ajst-20810	46	15	width	width	ADJ
ajst-20810	46	16	and	and	CCONJ
ajst-20810	46	17	depth	depth	NOUN
ajst-20810	46	18	.	.	PUNCT
ajst-20810	47	1	unlike	unlike	ADP
ajst-20810	47	2	standard	standard	ADJ
ajst-20810	47	3	artificial	artificial	ADJ
ajst-20810	47	4	neural	neural	ADJ
ajst-20810	47	5	networks	network	NOUN
ajst-20810	47	6	,	,	PUNCT
ajst-20810	47	7	the	the	DET
ajst-20810	47	8	neurons	neuron	NOUN
ajst-20810	47	9	in	in	ADP
ajst-20810	47	10	any	any	DET
ajst-20810	47	11	given	give	VERB
ajst-20810	47	12	layer	layer	NOUN
ajst-20810	47	13	of	of	ADP
ajst-20810	47	14	a	a	DET
ajst-20810	47	15	cnn	cnn	NOUN
ajst-20810	47	16	are	be	AUX
ajst-20810	47	17	connected	connect	VERB
ajst-20810	47	18	to	to	ADP
ajst-20810	47	19	only	only	ADV
ajst-20810	47	20	a	a	DET
ajst-20810	47	21	small	small	ADJ
ajst-20810	47	22	portion	portion	NOUN
ajst-20810	47	23	of	of	ADP
ajst-20810	47	24	the	the	DET
ajst-20810	47	25	area	area	NOUN
ajst-20810	47	26	of	of	ADP
ajst-20810	47	27	the	the	DET
ajst-20810	47	28	previous	previous	ADJ
ajst-20810	47	29	layer.the	layer.the	DET
ajst-20810	47	30	overall	overall	ADJ
ajst-20810	47	31	architecture	architecture	NOUN
ajst-20810	47	32	of	of	ADP
ajst-20810	47	33	a	a	DET
ajst-20810	47	34	cnn	cnn	PROPN
ajst-20810	47	35	consists	consist	VERB
ajst-20810	47	36	of	of	ADP
ajst-20810	47	37	a	a	DET
ajst-20810	47	38	convolutional	convolutional	ADJ
ajst-20810	47	39	layer	layer	NOUN
ajst-20810	47	40	,	,	PUNCT
ajst-20810	47	41	a	a	DET
ajst-20810	47	42	pooling	pool	VERB
ajst-20810	47	43	layer	layer	NOUN
ajst-20810	47	44	,	,	PUNCT
ajst-20810	47	45	and	and	CCONJ
ajst-20810	47	46	a	a	DET
ajst-20810	47	47	fully	fully	ADV
ajst-20810	47	48	connected	connect	VERB
ajst-20810	47	49	layer	layer	NOUN
ajst-20810	47	50	.	.	PUNCT
ajst-20810	48	1	these	these	DET
ajst-20810	48	2	layers	layer	NOUN
ajst-20810	48	3	can	can	AUX
ajst-20810	48	4	be	be	AUX
ajst-20810	48	5	stacked	stack	VERB
ajst-20810	48	6	on	on	ADP
ajst-20810	48	7	top	top	NOUN
ajst-20810	48	8	of	of	ADP
ajst-20810	48	9	each	each	DET
ajst-20810	48	10	other	other	ADJ
ajst-20810	48	11	,	,	PUNCT
ajst-20810	48	12	and	and	CCONJ
ajst-20810	48	13	when	when	SCONJ
ajst-20810	48	14	stacked	stack	VERB
ajst-20810	48	15	together	together	ADV
ajst-20810	48	16	,	,	PUNCT
ajst-20810	48	17	they	they	PRON
ajst-20810	48	18	make	make	VERB
ajst-20810	48	19	up	up	ADP
ajst-20810	48	20	a	a	DET
ajst-20810	48	21	cnn	cnn	NOUN
ajst-20810	48	22	architecture	architecture	NOUN
ajst-20810	48	23	.	.	PUNCT
ajst-20810	49	1	the	the	DET
ajst-20810	49	2	essence	essence	NOUN
ajst-20810	49	3	of	of	ADP
ajst-20810	49	4	convolutional	convolutional	ADJ
ajst-20810	49	5	neural	neural	ADJ
ajst-20810	49	6	network	network	NOUN
ajst-20810	49	7	is	be	AUX
ajst-20810	49	8	a	a	DET
ajst-20810	49	9	multi	multi	ADJ
ajst-20810	49	10	-	-	ADJ
ajst-20810	49	11	layer	layer	ADJ
ajst-20810	49	12	perceptual	perceptual	ADJ
ajst-20810	49	13	machine	machine	NOUN
ajst-20810	49	14	.	.	PUNCT
ajst-20810	50	1	compared	compare	VERB
ajst-20810	50	2	with	with	ADP
ajst-20810	50	3	fully	fully	ADV
ajst-20810	50	4	connected	connect	VERB
ajst-20810	50	5	neural	neural	ADJ
ajst-20810	50	6	network	network	NOUN
ajst-20810	50	7	,	,	PUNCT
ajst-20810	50	8	convolutional	convolutional	ADJ
ajst-20810	50	9	neural	neural	ADJ
ajst-20810	50	10	network	network	NOUN
ajst-20810	50	11	can	can	AUX
ajst-20810	50	12	effectively	effectively	ADV
ajst-20810	50	13	reduce	reduce	VERB
ajst-20810	50	14	the	the	DET
ajst-20810	50	15	size	size	NOUN
ajst-20810	50	16	of	of	ADP
ajst-20810	50	17	training	training	NOUN
ajst-20810	50	18	parameters	parameter	NOUN
ajst-20810	50	19	in	in	ADP
ajst-20810	50	20	the	the	DET
ajst-20810	50	21	network	network	NOUN
ajst-20810	50	22	by	by	ADP
ajst-20810	50	23	setting	set	VERB
ajst-20810	50	24	local	local	ADJ
ajst-20810	50	25	receptive	receptive	ADJ
ajst-20810	50	26	fields	field	NOUN
ajst-20810	50	27	,	,	PUNCT
ajst-20810	50	28	weight	weight	NOUN
ajst-20810	50	29	sharing	sharing	NOUN
ajst-20810	50	30	,	,	PUNCT
ajst-20810	50	31	pooling	pool	VERB
ajst-20810	50	32	layer	layer	NOUN
ajst-20810	50	33	and	and	CCONJ
ajst-20810	50	34	other	other	ADJ
ajst-20810	50	35	operations	operation	NOUN
ajst-20810	50	36	,	,	PUNCT
ajst-20810	50	37	which	which	PRON
ajst-20810	50	38	can	can	AUX
ajst-20810	50	39	greatly	greatly	ADV
ajst-20810	50	40	reduce	reduce	VERB
ajst-20810	50	41	the	the	DET
ajst-20810	50	42	amount	amount	NOUN
ajst-20810	50	43	of	of	ADP
ajst-20810	50	44	computation	computation	NOUN
ajst-20810	50	45	and	and	CCONJ
ajst-20810	50	46	the	the	DET
ajst-20810	50	47	complexity	complexity	NOUN
ajst-20810	50	48	of	of	ADP
ajst-20810	50	49	the	the	DET
ajst-20810	50	50	model	model	NOUN
ajst-20810	50	51	,	,	PUNCT
ajst-20810	50	52	and	and	CCONJ
ajst-20810	50	53	therefore	therefore	ADV
ajst-20810	50	54	it	it	PRON
ajst-20810	50	55	is	be	AUX
ajst-20810	50	56	especially	especially	ADV
ajst-20810	50	57	suitable	suitable	ADJ
ajst-20810	50	58	for	for	ADP
ajst-20810	50	59	use	use	NOUN
ajst-20810	50	60	in	in	ADP
ajst-20810	50	61	image	image	NOUN
ajst-20810	50	62	recognition	recognition	NOUN
ajst-20810	50	63	and	and	CCONJ
ajst-20810	50	64	feature	feature	NOUN
ajst-20810	50	65	extraction	extraction	NOUN
ajst-20810	50	66	tasks	task	NOUN
ajst-20810	50	67	as	as	ADP
ajst-20810	50	68	a	a	DET
ajst-20810	50	69	feature	feature	NOUN
ajst-20810	50	70	extractor	extractor	NOUN
ajst-20810	50	71	for	for	ADP
ajst-20810	50	72	images	image	NOUN
ajst-20810	50	73	.	.	PUNCT
ajst-20810	51	1	3	3	X
ajst-20810	51	2	.	.	X
ajst-20810	51	3	resnet	resnet	NOUN
ajst-20810	51	4	-	-	PUNCT
ajst-20810	51	5	based	base	VERB
ajst-20810	51	6	image	image	NOUN
ajst-20810	51	7	classification	classification	NOUN
ajst-20810	51	8	3.1	3.1	NUM
ajst-20810	51	9	.	.	PUNCT
ajst-20810	52	1	feed	feed	NOUN
ajst-20810	52	2	-	-	PUNCT
ajst-20810	52	3	forward	forward	ADV
ajst-20810	52	4	neural	neural	ADJ
ajst-20810	52	5	network	network	NOUN
ajst-20810	52	6	feedforward	feedforward	NOUN
ajst-20810	52	7	neural	neural	ADJ
ajst-20810	52	8	networks	network	NOUN
ajst-20810	52	9	are	be	AUX
ajst-20810	52	10	one	one	NUM
ajst-20810	52	11	of	of	ADP
ajst-20810	52	12	the	the	DET
ajst-20810	52	13	most	most	ADV
ajst-20810	52	14	commonly	commonly	ADV
ajst-20810	52	15	used	use	VERB
ajst-20810	52	16	function	function	NOUN
ajst-20810	52	17	approximation	approximation	NOUN
ajst-20810	52	18	techniques	technique	NOUN
ajst-20810	52	19	and	and	CCONJ
ajst-20810	52	20	have	have	AUX
ajst-20810	52	21	been	be	AUX
ajst-20810	52	22	applied	apply	VERB
ajst-20810	52	23	to	to	ADP
ajst-20810	52	24	problems	problem	NOUN
ajst-20810	52	25	arising	arise	VERB
ajst-20810	52	26	from	from	ADP
ajst-20810	52	27	a	a	DET
ajst-20810	52	28	variety	variety	NOUN
ajst-20810	52	29	of	of	ADP
ajst-20810	52	30	disciplines	discipline	NOUN
ajst-20810	52	31	.	.	PUNCT
ajst-20810	53	1	feedforward	feedforward	ADJ
ajst-20810	53	2	neural	neural	ADJ
ajst-20810	53	3	network	network	NOUN
ajst-20810	53	4	is	be	AUX
ajst-20810	53	5	a	a	DET
ajst-20810	53	6	deep	deep	ADJ
ajst-20810	53	7	learning	learning	NOUN
ajst-20810	53	8	model	model	NOUN
ajst-20810	53	9	with	with	ADP
ajst-20810	53	10	a	a	DET
ajst-20810	53	11	unidirectional	unidirectional	ADJ
ajst-20810	53	12	multilayer	multilayer	ADJ
ajst-20810	53	13	structure	structure	NOUN
ajst-20810	53	14	in	in	ADP
ajst-20810	53	15	which	which	PRON
ajst-20810	53	16	each	each	DET
ajst-20810	53	17	layer	layer	NOUN
ajst-20810	53	18	packs	pack	VERB
ajst-20810	53	19	a	a	DET
ajst-20810	53	20	number	number	NOUN
ajst-20810	53	21	of	of	ADP
ajst-20810	53	22	neurons	neuron	NOUN
ajst-20810	53	23	.	.	PUNCT
ajst-20810	54	1	its	its	PRON
ajst-20810	54	2	zero	zero	NUM
ajst-20810	54	3	-	-	PUNCT
ajst-20810	54	4	layer	layer	NOUN
ajst-20810	54	5	structure	structure	NOUN
ajst-20810	54	6	is	be	AUX
ajst-20810	54	7	called	call	VERB
ajst-20810	54	8	the	the	DET
ajst-20810	54	9	input	input	NOUN
ajst-20810	54	10	layer	layer	NOUN
ajst-20810	54	11	,	,	PUNCT
ajst-20810	54	12	which	which	PRON
ajst-20810	54	13	is	be	AUX
ajst-20810	54	14	the	the	DET
ajst-20810	54	15	location	location	NOUN
ajst-20810	54	16	of	of	ADP
ajst-20810	54	17	the	the	DET
ajst-20810	54	18	input	input	NOUN
ajst-20810	54	19	of	of	ADP
ajst-20810	54	20	the	the	DET
ajst-20810	54	21	original	original	ADJ
ajst-20810	54	22	data	datum	NOUN
ajst-20810	54	23	,	,	PUNCT
ajst-20810	54	24	the	the	DET
ajst-20810	54	25	hidden	hide	VERB
ajst-20810	54	26	layer	layer	NOUN
ajst-20810	54	27	is	be	AUX
ajst-20810	54	28	an	an	DET
ajst-20810	54	29	intermediate	intermediate	ADJ
ajst-20810	54	30	layer	layer	NOUN
ajst-20810	54	31	or	or	CCONJ
ajst-20810	54	32	layers	layer	NOUN
ajst-20810	54	33	after	after	ADP
ajst-20810	54	34	the	the	DET
ajst-20810	54	35	zero	zero	NUM
ajst-20810	54	36	layer	layer	NOUN
ajst-20810	54	37	,	,	PUNCT
ajst-20810	54	38	the	the	DET
ajst-20810	54	39	number	number	NOUN
ajst-20810	54	40	of	of	ADP
ajst-20810	54	41	layers	layer	NOUN
ajst-20810	54	42	of	of	ADP
ajst-20810	54	43	the	the	DET
ajst-20810	54	44	hidden	hide	VERB
ajst-20810	54	45	layer	layer	NOUN
ajst-20810	54	46	determines	determine	VERB
ajst-20810	54	47	the	the	DET
ajst-20810	54	48	depth	depth	NOUN
ajst-20810	54	49	of	of	ADP
ajst-20810	54	50	the	the	DET
ajst-20810	54	51	network	network	NOUN
ajst-20810	54	52	,	,	PUNCT
ajst-20810	54	53	and	and	CCONJ
ajst-20810	54	54	the	the	DET
ajst-20810	54	55	hidden	hide	VERB
ajst-20810	54	56	layer	layer	NOUN
ajst-20810	54	57	is	be	AUX
ajst-20810	54	58	followed	follow	VERB
ajst-20810	54	59	by	by	ADP
ajst-20810	54	60	the	the	DET
ajst-20810	54	61	output	output	NOUN
ajst-20810	54	62	layer	layer	NOUN
ajst-20810	54	63	,	,	PUNCT
ajst-20810	54	64	which	which	PRON
ajst-20810	54	65	represents	represent	VERB
ajst-20810	54	66	the	the	DET
ajst-20810	54	67	classification	classification	NOUN
ajst-20810	54	68	probability	probability	NOUN
ajst-20810	54	69	in	in	ADP
ajst-20810	54	70	the	the	DET
ajst-20810	54	71	classification	classification	NOUN
ajst-20810	54	72	task	task	NOUN
ajst-20810	54	73	.	.	PUNCT
ajst-20810	55	1	as	as	SCONJ
ajst-20810	55	2	shown	show	VERB
ajst-20810	55	3	in	in	ADP
ajst-20810	55	4	figure	figure	NOUN
ajst-20810	55	5	1	1	NUM
ajst-20810	55	6	,	,	PUNCT
ajst-20810	55	7	this	this	PRON
ajst-20810	55	8	is	be	AUX
ajst-20810	55	9	a	a	DET
ajst-20810	55	10	typical	typical	ADJ
ajst-20810	55	11	multi	multi	ADJ
ajst-20810	55	12	-	-	ADJ
ajst-20810	55	13	layer	layer	ADJ
ajst-20810	55	14	feedforward	feedforward	NOUN
ajst-20810	55	15	neural	neural	ADJ
ajst-20810	55	16	network	network	NOUN
ajst-20810	55	17	,	,	PUNCT
ajst-20810	55	18	this	this	DET
ajst-20810	55	19	neural	neural	ADJ
ajst-20810	55	20	network	network	NOUN
ajst-20810	55	21	has	have	VERB
ajst-20810	55	22	four	four	NUM
ajst-20810	55	23	neurons	neuron	NOUN
ajst-20810	55	24	in	in	ADP
ajst-20810	55	25	the	the	DET
ajst-20810	55	26	input	input	NOUN
ajst-20810	55	27	layer	layer	NOUN
ajst-20810	55	28	,	,	PUNCT
ajst-20810	55	29	the	the	DET
ajst-20810	55	30	neurons	neuron	NOUN
ajst-20810	55	31	are	be	AUX
ajst-20810	55	32	connected	connect	VERB
ajst-20810	55	33	to	to	ADP
ajst-20810	55	34	each	each	DET
ajst-20810	55	35	other	other	ADJ
ajst-20810	55	36	after	after	ADP
ajst-20810	55	37	passing	pass	VERB
ajst-20810	55	38	through	through	ADP
ajst-20810	55	39	the	the	DET
ajst-20810	55	40	input	input	NOUN
ajst-20810	55	41	layer	layer	NOUN
ajst-20810	55	42	,	,	PUNCT
ajst-20810	55	43	and	and	CCONJ
ajst-20810	55	44	then	then	ADV
ajst-20810	55	45	they	they	PRON
ajst-20810	55	46	become	become	VERB
ajst-20810	55	47	five	five	NUM
ajst-20810	55	48	when	when	SCONJ
ajst-20810	55	49	they	they	PRON
ajst-20810	55	50	reach	reach	VERB
ajst-20810	55	51	the	the	DET
ajst-20810	55	52	first	first	ADJ
ajst-20810	55	53	hidden	hide	VERB
ajst-20810	55	54	layer	layer	NOUN
ajst-20810	55	55	,	,	PUNCT
ajst-20810	55	56	and	and	CCONJ
ajst-20810	55	57	then	then	ADV
ajst-20810	55	58	after	after	ADP
ajst-20810	55	59	the	the	DET
ajst-20810	55	60	first	first	ADJ
ajst-20810	55	61	hidden	hide	VERB
ajst-20810	55	62	layer	layer	NOUN
ajst-20810	55	63	,	,	PUNCT
ajst-20810	55	64	the	the	DET
ajst-20810	55	65	neurons	neuron	NOUN
ajst-20810	55	66	are	be	AUX
ajst-20810	55	67	reduced	reduce	VERB
ajst-20810	55	68	to	to	ADP
ajst-20810	55	69	three	three	NUM
ajst-20810	55	70	,	,	PUNCT
ajst-20810	55	71	and	and	CCONJ
ajst-20810	55	72	the	the	DET
ajst-20810	55	73	neural	neural	ADJ
ajst-20810	55	74	network	network	NOUN
ajst-20810	55	75	extracts	extract	NOUN
ajst-20810	55	76	deeper	deep	ADJ
ajst-20810	55	77	features	feature	NOUN
ajst-20810	55	78	in	in	ADP
ajst-20810	55	79	these	these	DET
ajst-20810	55	80	processes	process	NOUN
ajst-20810	55	81	,	,	PUNCT
ajst-20810	55	82	and	and	CCONJ
ajst-20810	55	83	then	then	ADV
ajst-20810	55	84	finally	finally	ADV
ajst-20810	55	85	outputs	output	VERB
ajst-20810	55	86	the	the	DET
ajst-20810	55	87	classification	classification	NOUN
ajst-20810	55	88	results	result	NOUN
ajst-20810	55	89	through	through	ADP
ajst-20810	55	90	the	the	DET
ajst-20810	55	91	output	output	NOUN
ajst-20810	55	92	layer	layer	NOUN
ajst-20810	55	93	.	.	PUNCT
ajst-20810	56	1	figure	figure	NOUN
ajst-20810	56	2	1	1	NUM
ajst-20810	56	3	.	.	PUNCT
ajst-20810	56	4	multilayer	multilayer	ADJ
ajst-20810	56	5	feed	feed	NOUN
ajst-20810	56	6	-	-	PUNCT
ajst-20810	56	7	forward	forward	ADV
ajst-20810	56	8	neural	neural	ADJ
ajst-20810	56	9	network	network	NOUN
ajst-20810	56	10	structure	structure	NOUN
ajst-20810	56	11	feedforward	feedforward	NOUN
ajst-20810	56	12	neural	neural	ADJ
ajst-20810	56	13	network	network	NOUN
ajst-20810	56	14	not	not	PART
ajst-20810	56	15	only	only	ADV
ajst-20810	56	16	possesses	possess	VERB
ajst-20810	56	17	the	the	DET
ajst-20810	56	18	outstanding	outstanding	ADJ
ajst-20810	56	19	features	feature	NOUN
ajst-20810	56	20	of	of	ADP
ajst-20810	56	21	simple	simple	ADJ
ajst-20810	56	22	structure	structure	NOUN
ajst-20810	56	23	and	and	CCONJ
ajst-20810	56	24	wide	wide	ADJ
ajst-20810	56	25	range	range	NOUN
ajst-20810	56	26	of	of	ADP
ajst-20810	56	27	application	application	NOUN
ajst-20810	56	28	scenarios	scenario	NOUN
ajst-20810	56	29	,	,	PUNCT
ajst-20810	56	30	but	but	CCONJ
ajst-20810	56	31	also	also	ADV
ajst-20810	56	32	it	it	PRON
ajst-20810	56	33	is	be	AUX
ajst-20810	56	34	easy	easy	ADJ
ajst-20810	56	35	to	to	PART
ajst-20810	56	36	fit	fit	VERB
ajst-20810	56	37	a	a	DET
ajst-20810	56	38	variety	variety	NOUN
ajst-20810	56	39	of	of	ADP
ajst-20810	56	40	continuous	continuous	ADJ
ajst-20810	56	41	functions	function	NOUN
ajst-20810	56	42	,	,	PUNCT
ajst-20810	56	43	and	and	CCONJ
ajst-20810	56	44	its	its	PRON
ajst-20810	56	45	representation	representation	NOUN
ajst-20810	56	46	ability	ability	NOUN
ajst-20810	56	47	is	be	AUX
ajst-20810	56	48	very	very	ADV
ajst-20810	56	49	powerful	powerful	ADJ
ajst-20810	56	50	to	to	PART
ajst-20810	56	51	learn	learn	VERB
ajst-20810	56	52	the	the	DET
ajst-20810	56	53	data	data	NOUN
ajst-20810	56	54	laws	law	NOUN
ajst-20810	56	55	of	of	ADP
ajst-20810	56	56	any	any	DET
ajst-20810	56	57	data	datum	NOUN
ajst-20810	56	58	set	set	VERB
ajst-20810	56	59	.	.	PUNCT
ajst-20810	57	1	in	in	ADP
ajst-20810	57	2	terms	term	NOUN
ajst-20810	57	3	of	of	ADP
ajst-20810	57	4	computation	computation	NOUN
ajst-20810	57	5	,	,	PUNCT
ajst-20810	57	6	feedforward	feedforward	NOUN
ajst-20810	57	7	neural	neural	ADJ
ajst-20810	57	8	networks	network	NOUN
ajst-20810	57	9	lack	lack	VERB
ajst-20810	57	10	rich	rich	ADJ
ajst-20810	57	11	dynamic	dynamic	ADJ
ajst-20810	57	12	behaviour	behaviour	NOUN
ajst-20810	57	13	.	.	PUNCT
ajst-20810	58	1	and	and	CCONJ
ajst-20810	58	2	from	from	ADP
ajst-20810	58	3	a	a	DET
ajst-20810	58	4	system	system	NOUN
ajst-20810	58	5	perspective	perspective	NOUN
ajst-20810	58	6	,	,	PUNCT
ajst-20810	58	7	the	the	DET
ajst-20810	58	8	nonlinear	nonlinear	ADJ
ajst-20810	58	9	mapping	mapping	NOUN
ajst-20810	58	10	ability	ability	NOUN
ajst-20810	58	11	of	of	ADP
ajst-20810	58	12	feedforward	feedforward	ADJ
ajst-20810	58	13	neural	neural	ADJ
ajst-20810	58	14	networks	network	NOUN
ajst-20810	58	15	is	be	AUX
ajst-20810	58	16	static	static	ADJ
ajst-20810	58	17	.	.	PUNCT
ajst-20810	59	1	however	however	ADV
ajst-20810	59	2	,	,	PUNCT
ajst-20810	59	3	it	it	PRON
ajst-20810	59	4	has	have	VERB
ajst-20810	59	5	a	a	DET
ajst-20810	59	6	powerful	powerful	ADJ
ajst-20810	59	7	nonlinear	nonlinear	ADJ
ajst-20810	59	8	processing	processing	NOUN
ajst-20810	59	9	capability	capability	NOUN
ajst-20810	59	10	,	,	PUNCT
ajst-20810	59	11	the	the	DET
ajst-20810	59	12	implementation	implementation	NOUN
ajst-20810	59	13	of	of	ADP
ajst-20810	59	14	which	which	PRON
ajst-20810	59	15	is	be	AUX
ajst-20810	59	16	obtained	obtain	VERB
ajst-20810	59	17	by	by	ADP
ajst-20810	59	18	simple	simple	ADJ
ajst-20810	59	19	combinatorial	combinatorial	ADJ
ajst-20810	59	20	mapping	mapping	NOUN
ajst-20810	59	21	of	of	ADP
ajst-20810	59	22	neurons	neuron	NOUN
ajst-20810	59	23	.	.	PUNCT
ajst-20810	60	1	as	as	ADV
ajst-20810	60	2	far	far	ADV
ajst-20810	60	3	as	as	SCONJ
ajst-20810	60	4	most	most	ADJ
ajst-20810	60	5	of	of	ADP
ajst-20810	60	6	the	the	DET
ajst-20810	60	7	feedforward	feedforward	ADJ
ajst-20810	60	8	neural	neural	ADJ
ajst-20810	60	9	networks	network	NOUN
ajst-20810	60	10	are	be	AUX
ajst-20810	60	11	concerned	concerned	ADJ
ajst-20810	60	12	they	they	PRON
ajst-20810	60	13	are	be	AUX
ajst-20810	60	14	a	a	DET
ajst-20810	60	15	relatively	relatively	ADV
ajst-20810	60	16	good	good	ADJ
ajst-20810	60	17	learning	learn	VERB
ajst-20810	60	18	network	network	NOUN
ajst-20810	60	19	with	with	ADP
ajst-20810	60	20	better	well	ADJ
ajst-20810	60	21	performance	performance	NOUN
ajst-20810	60	22	than	than	ADP
ajst-20810	60	23	the	the	DET
ajst-20810	60	24	usual	usual	ADJ
ajst-20810	60	25	feedback	feedback	NOUN
ajst-20810	60	26	networks	network	NOUN
ajst-20810	60	27	in	in	ADP
ajst-20810	60	28	the	the	DET
ajst-20810	60	29	field	field	NOUN
ajst-20810	60	30	of	of	ADP
ajst-20810	60	31	image	image	NOUN
ajst-20810	60	32	classification	classification	NOUN
ajst-20810	60	33	.	.	PUNCT
ajst-20810	61	1	however	however	ADV
ajst-20810	61	2	,	,	PUNCT
ajst-20810	61	3	in	in	ADP
ajst-20810	61	4	the	the	DET
ajst-20810	61	5	field	field	NOUN
ajst-20810	61	6	of	of	ADP
ajst-20810	61	7	deep	deep	ADJ
ajst-20810	61	8	learning	learning	NOUN
ajst-20810	61	9	,	,	PUNCT
ajst-20810	61	10	feedforward	feedforward	ADJ
ajst-20810	61	11	neural	neural	ADJ
ajst-20810	61	12	networks	network	NOUN
ajst-20810	61	13	also	also	ADV
ajst-20810	61	14	have	have	VERB
ajst-20810	61	15	flaws	flaw	NOUN
ajst-20810	61	16	.	.	PUNCT
ajst-20810	62	1	feedforward	feedforward	ADJ
ajst-20810	62	2	neural	neural	ADJ
ajst-20810	62	3	networks	network	NOUN
ajst-20810	62	4	in	in	ADP
ajst-20810	62	5	the	the	DET
ajst-20810	62	6	training	training	NOUN
ajst-20810	62	7	aspect	aspect	NOUN
ajst-20810	62	8	,	,	PUNCT
ajst-20810	62	9	using	use	VERB
ajst-20810	62	10	the	the	DET
ajst-20810	62	11	traditional	traditional	ADJ
ajst-20810	62	12	gradient	gradient	ADJ
ajst-20810	62	13	descent	descent	NOUN
ajst-20810	62	14	method	method	NOUN
ajst-20810	62	15	,	,	PUNCT
ajst-20810	62	16	which	which	PRON
ajst-20810	62	17	makes	make	VERB
ajst-20810	62	18	the	the	DET
ajst-20810	62	19	training	training	NOUN
ajst-20810	62	20	rate	rate	NOUN
ajst-20810	62	21	is	be	AUX
ajst-20810	62	22	lower	low	ADJ
ajst-20810	62	23	,	,	PUNCT
ajst-20810	62	24	the	the	DET
ajst-20810	62	25	training	training	NOUN
ajst-20810	62	26	time	time	NOUN
ajst-20810	62	27	is	be	AUX
ajst-20810	62	28	longer	long	ADJ
ajst-20810	62	29	.	.	PUNCT
ajst-20810	63	1	at	at	ADP
ajst-20810	63	2	the	the	DET
ajst-20810	63	3	same	same	ADJ
ajst-20810	63	4	time	time	NOUN
ajst-20810	63	5	,	,	PUNCT
ajst-20810	63	6	the	the	DET
ajst-20810	63	7	learning	learn	VERB
ajst-20810	63	8	rate	rate	NOUN
ajst-20810	63	9	chosen	choose	VERB
ajst-20810	63	10	by	by	ADP
ajst-20810	63	11	the	the	DET
ajst-20810	63	12	model	model	NOUN
ajst-20810	63	13	is	be	AUX
ajst-20810	63	14	not	not	PART
ajst-20810	63	15	flexible	flexible	ADJ
ajst-20810	63	16	enough	enough	ADV
ajst-20810	63	17	,	,	PUNCT
ajst-20810	63	18	and	and	CCONJ
ajst-20810	63	19	the	the	DET
ajst-20810	63	20	learning	learning	NOUN
ajst-20810	63	21	rate	rate	NOUN
ajst-20810	63	22	is	be	AUX
ajst-20810	63	23	closely	closely	ADV
ajst-20810	63	24	related	relate	VERB
ajst-20810	63	25	to	to	ADP
ajst-20810	63	26	the	the	DET
ajst-20810	63	27	performance	performance	NOUN
ajst-20810	63	28	of	of	ADP
ajst-20810	63	29	the	the	DET
ajst-20810	63	30	neural	neural	ADJ
ajst-20810	63	31	network	network	NOUN
ajst-20810	63	32	,	,	PUNCT
ajst-20810	63	33	too	too	ADV
ajst-20810	63	34	big	big	ADJ
ajst-20810	63	35	or	or	CCONJ
ajst-20810	63	36	too	too	ADV
ajst-20810	63	37	small	small	ADJ
ajst-20810	63	38	will	will	AUX
ajst-20810	63	39	lead	lead	VERB
ajst-20810	63	40	to	to	ADP
ajst-20810	63	41	bad	bad	ADJ
ajst-20810	63	42	consequences	consequence	NOUN
ajst-20810	63	43	.	.	PUNCT
ajst-20810	64	1	180	180	NUM
ajst-20810	64	2	3.2	3.2	NUM
ajst-20810	64	3	.	.	PUNCT
ajst-20810	65	1	resnet	resnet	NOUN
ajst-20810	65	2	model	model	NOUN
ajst-20810	65	3	the	the	DET
ajst-20810	65	4	core	core	NOUN
ajst-20810	65	5	concept	concept	NOUN
ajst-20810	65	6	of	of	ADP
ajst-20810	65	7	resnet	resnet	NOUN
ajst-20810	65	8	is	be	AUX
ajst-20810	65	9	to	to	PART
ajst-20810	65	10	add	add	VERB
ajst-20810	65	11	a	a	DET
ajst-20810	65	12	constant	constant	ADJ
ajst-20810	65	13	mapping	mapping	NOUN
ajst-20810	65	14	path	path	NOUN
ajst-20810	65	15	to	to	ADP
ajst-20810	65	16	the	the	DET
ajst-20810	65	17	neural	neural	ADJ
ajst-20810	65	18	network	network	NOUN
ajst-20810	65	19	,	,	PUNCT
ajst-20810	65	20	as	as	SCONJ
ajst-20810	65	21	shown	show	VERB
ajst-20810	65	22	in	in	ADP
ajst-20810	65	23	figure	figure	NOUN
ajst-20810	65	24	2	2	NUM
ajst-20810	65	25	,	,	PUNCT
ajst-20810	65	26	where	where	SCONJ
ajst-20810	65	27	the	the	DET
ajst-20810	65	28	input	input	NOUN
ajst-20810	65	29	data	datum	NOUN
ajst-20810	65	30	is	be	AUX
ajst-20810	65	31	passed	pass	VERB
ajst-20810	65	32	through	through	ADP
ajst-20810	65	33	two	two	NUM
ajst-20810	65	34	consecutive	consecutive	ADJ
ajst-20810	65	35	network	network	NOUN
ajst-20810	65	36	layers	layer	NOUN
ajst-20810	65	37	to	to	PART
ajst-20810	65	38	get	get	VERB
ajst-20810	65	39	a	a	DET
ajst-20810	65	40	non	non	ADJ
ajst-20810	65	41	-	-	ADJ
ajst-20810	65	42	linearly	linearly	ADV
ajst-20810	65	43	mapped	map	VERB
ajst-20810	65	44	output	output	NOUN
ajst-20810	65	45	,	,	PUNCT
ajst-20810	65	46	and	and	CCONJ
ajst-20810	65	47	added	add	VERB
ajst-20810	65	48	to	to	ADP
ajst-20810	65	49	the	the	DET
ajst-20810	65	50	original	original	ADJ
ajst-20810	65	51	input	input	NOUN
ajst-20810	65	52	to	to	PART
ajst-20810	65	53	get	get	VERB
ajst-20810	65	54	the	the	DET
ajst-20810	65	55	final	final	ADJ
ajst-20810	65	56	residual	residual	ADJ
ajst-20810	65	57	output	output	NOUN
ajst-20810	65	58	.	.	PUNCT
ajst-20810	66	1	that	that	PRON
ajst-20810	66	2	is	be	AUX
ajst-20810	66	3	,	,	PUNCT
ajst-20810	66	4	the	the	DET
ajst-20810	66	5	addition	addition	NOUN
ajst-20810	66	6	of	of	ADP
ajst-20810	66	7	a	a	DET
ajst-20810	66	8	constant	constant	ADJ
ajst-20810	66	9	mapping	mapping	NOUN
ajst-20810	66	10	converts	convert	VERB
ajst-20810	66	11	the	the	DET
ajst-20810	66	12	original	original	ADJ
ajst-20810	66	13	function	function	NOUN
ajst-20810	66	14	(	(	PUNCT
ajst-20810	66	15	)	)	PUNCT
ajst-20810	66	16	h	h	NOUN
ajst-20810	66	17	x	x	NOUN
ajst-20810	66	18	that	that	PRON
ajst-20810	66	19	needs	need	VERB
ajst-20810	66	20	to	to	PART
ajst-20810	66	21	be	be	AUX
ajst-20810	66	22	learned	learn	VERB
ajst-20810	66	23	into	into	ADP
ajst-20810	66	24	(	(	PUNCT
ajst-20810	66	25	)	)	PUNCT
ajst-20810	66	26	f	f	NOUN
ajst-20810	67	1	x	x	NOUN
ajst-20810	67	2	x+	x+	X
ajst-20810	67	3	,	,	PUNCT
ajst-20810	67	4	and	and	CCONJ
ajst-20810	67	5	the	the	DET
ajst-20810	67	6	hypothetical	hypothetical	ADJ
ajst-20810	67	7	optimisation	optimisation	NOUN
ajst-20810	67	8	of	of	ADP
ajst-20810	67	9	(	(	PUNCT
ajst-20810	67	10	)	)	PUNCT
ajst-20810	67	11	f	f	X
ajst-20810	67	12	x	x	PRON
ajst-20810	67	13	would	would	AUX
ajst-20810	67	14	be	be	AUX
ajst-20810	67	15	much	much	ADV
ajst-20810	67	16	simpler	simple	ADJ
ajst-20810	67	17	than	than	ADP
ajst-20810	67	18	(	(	PUNCT
ajst-20810	67	19	)	)	PUNCT
ajst-20810	67	20	h	h	NOUN
ajst-20810	68	1	x	x	X
ajst-20810	68	2	,	,	PUNCT
ajst-20810	68	3	which	which	PRON
ajst-20810	68	4	would	would	AUX
ajst-20810	68	5	be	be	AUX
ajst-20810	68	6	the	the	DET
ajst-20810	68	7	same	same	ADJ
ajst-20810	68	8	for	for	ADP
ajst-20810	68	9	both	both	CCONJ
ajst-20810	68	10	representations	representation	NOUN
ajst-20810	68	11	but	but	CCONJ
ajst-20810	68	12	not	not	PART
ajst-20810	68	13	the	the	DET
ajst-20810	68	14	same	same	ADJ
ajst-20810	68	15	level	level	NOUN
ajst-20810	68	16	of	of	ADP
ajst-20810	68	17	difficulty	difficulty	NOUN
ajst-20810	68	18	to	to	PART
ajst-20810	68	19	optimise	optimise	VERB
ajst-20810	68	20	.	.	PUNCT
ajst-20810	69	1	the	the	DET
ajst-20810	69	2	emergence	emergence	NOUN
ajst-20810	69	3	of	of	ADP
ajst-20810	69	4	this	this	DET
ajst-20810	69	5	model	model	NOUN
ajst-20810	69	6	has	have	AUX
ajst-20810	69	7	allowed	allow	VERB
ajst-20810	69	8	the	the	DET
ajst-20810	69	9	network	network	NOUN
ajst-20810	69	10	model	model	NOUN
ajst-20810	69	11	depth	depth	NOUN
ajst-20810	69	12	to	to	PART
ajst-20810	69	13	be	be	AUX
ajst-20810	69	14	unrestricted	unrestricted	ADJ
ajst-20810	69	15	over	over	ADP
ajst-20810	69	16	a	a	DET
ajst-20810	69	17	wide	wide	ADJ
ajst-20810	69	18	range	range	NOUN
ajst-20810	69	19	(	(	PUNCT
ajst-20810	69	20	currently	currently	ADV
ajst-20810	69	21	up	up	ADP
ajst-20810	69	22	to	to	PART
ajst-20810	69	23	1000	1000	NUM
ajst-20810	69	24	layers	layer	NOUN
ajst-20810	69	25	or	or	CCONJ
ajst-20810	69	26	more	more	ADJ
ajst-20810	69	27	)	)	PUNCT
ajst-20810	69	28	and	and	CCONJ
ajst-20810	69	29	has	have	AUX
ajst-20810	69	30	had	have	VERB
ajst-20810	69	31	a	a	DET
ajst-20810	69	32	profound	profound	ADJ
ajst-20810	69	33	impact	impact	NOUN
ajst-20810	69	34	on	on	ADP
ajst-20810	69	35	the	the	DET
ajst-20810	69	36	subsequent	subsequent	ADJ
ajst-20810	69	37	development	development	NOUN
ajst-20810	69	38	of	of	ADP
ajst-20810	69	39	convolutional	convolutional	ADJ
ajst-20810	69	40	neural	neural	ADJ
ajst-20810	69	41	networks	network	NOUN
ajst-20810	69	42	.	.	PUNCT
ajst-20810	70	1	the	the	DET
ajst-20810	70	2	idea	idea	NOUN
ajst-20810	70	3	has	have	AUX
ajst-20810	70	4	gone	go	VERB
ajst-20810	70	5	through	through	ADP
ajst-20810	70	6	a	a	DET
ajst-20810	70	7	short	short	ADJ
ajst-20810	70	8	period	period	NOUN
ajst-20810	70	9	of	of	ADP
ajst-20810	70	10	time	time	NOUN
ajst-20810	70	11	from	from	ADP
ajst-20810	70	12	its	its	PRON
ajst-20810	70	13	creation	creation	NOUN
ajst-20810	70	14	to	to	ADP
ajst-20810	70	15	its	its	PRON
ajst-20810	70	16	practical	practical	ADJ
ajst-20810	70	17	application	application	NOUN
ajst-20810	70	18	.	.	PUNCT
ajst-20810	71	1	figure	figure	NOUN
ajst-20810	71	2	2	2	NUM
ajst-20810	71	3	.	.	PUNCT
ajst-20810	71	4	resnet	resnet	PROPN
ajst-20810	71	5	's	's	PART
ajst-20810	71	6	residual	residual	ADJ
ajst-20810	71	7	learning	learning	NOUN
ajst-20810	71	8	module	module	NOUN
ajst-20810	71	9	in	in	ADP
ajst-20810	71	10	image	image	NOUN
ajst-20810	71	11	processing	processing	NOUN
ajst-20810	71	12	,	,	PUNCT
ajst-20810	71	13	vlad	vlad	VERB
ajst-20810	71	14	(	(	PUNCT
ajst-20810	71	15	vector	vector	NOUN
ajst-20810	71	16	of	of	ADP
ajst-20810	71	17	locally	locally	ADV
ajst-20810	71	18	aggregated	aggregate	VERB
ajst-20810	71	19	descriptors)[10	descriptors)[10	NOUN
ajst-20810	71	20	]	]	PUNCT
ajst-20810	71	21	is	be	AUX
ajst-20810	71	22	a	a	DET
ajst-20810	71	23	representation	representation	NOUN
ajst-20810	71	24	encoded	encode	VERB
ajst-20810	71	25	by	by	ADP
ajst-20810	71	26	a	a	DET
ajst-20810	71	27	vector	vector	NOUN
ajst-20810	71	28	of	of	ADP
ajst-20810	71	29	residuals	residual	NOUN
ajst-20810	71	30	,	,	PUNCT
ajst-20810	71	31	while	while	SCONJ
ajst-20810	71	32	fisher	fisher	PROPN
ajst-20810	71	33	vector[11	vector[11	PROPN
ajst-20810	71	34	]	]	PUNCT
ajst-20810	71	35	is	be	AUX
ajst-20810	71	36	described	describe	VERB
ajst-20810	71	37	as	as	ADP
ajst-20810	71	38	a	a	DET
ajst-20810	71	39	probabilistic	probabilistic	ADJ
ajst-20810	71	40	statistical	statistical	ADJ
ajst-20810	71	41	version	version	NOUN
ajst-20810	71	42	of	of	ADP
ajst-20810	71	43	vlad	vlad	VERB
ajst-20810	71	44	.	.	PUNCT
ajst-20810	72	1	residual	residual	ADJ
ajst-20810	72	2	-	-	PUNCT
ajst-20810	72	3	based	base	VERB
ajst-20810	72	4	fitting	fitting	ADJ
ajst-20810	72	5	as	as	ADV
ajst-20810	72	6	well	well	ADV
ajst-20810	72	7	as	as	ADP
ajst-20810	72	8	probabilistic	probabilistic	ADJ
ajst-20810	72	9	distribution	distribution	NOUN
ajst-20810	72	10	modelling	modelling	NOUN
ajst-20810	72	11	is	be	AUX
ajst-20810	72	12	a	a	DET
ajst-20810	72	13	powerful	powerful	ADJ
ajst-20810	72	14	in	in	ADP
ajst-20810	72	15	image	image	NOUN
ajst-20810	72	16	retrieval	retrieval	NOUN
ajst-20810	72	17	and	and	CCONJ
ajst-20810	72	18	classification	classification	NOUN
ajst-20810	72	19	tasks	task	NOUN
ajst-20810	72	20	data	data	NOUN
ajst-20810	72	21	shallow	shallow	ADJ
ajst-20810	72	22	characterisation	characterisation	NOUN
ajst-20810	72	23	techniques	technique	NOUN
ajst-20810	72	24	.	.	PUNCT
ajst-20810	73	1	in	in	ADP
ajst-20810	73	2	vector	vector	NOUN
ajst-20810	73	3	quantisation	quantisation	NOUN
ajst-20810	73	4	,	,	PUNCT
ajst-20810	73	5	encoding	encode	VERB
ajst-20810	73	6	residual	residual	ADJ
ajst-20810	73	7	vectors	vector	NOUN
ajst-20810	73	8	will	will	AUX
ajst-20810	73	9	present	present	VERB
ajst-20810	73	10	a	a	DET
ajst-20810	73	11	more	more	ADV
ajst-20810	73	12	efficient	efficient	ADJ
ajst-20810	73	13	performance	performance	NOUN
ajst-20810	73	14	than	than	ADP
ajst-20810	73	15	encoding	encode	VERB
ajst-20810	73	16	raw	raw	ADJ
ajst-20810	73	17	vectors	vector	NOUN
ajst-20810	73	18	directly	directly	ADV
ajst-20810	73	19	.	.	PUNCT
ajst-20810	74	1	in	in	ADP
ajst-20810	74	2	low	low	ADJ
ajst-20810	74	3	-	-	PUNCT
ajst-20810	74	4	level	level	NOUN
ajst-20810	74	5	vision	vision	NOUN
ajst-20810	74	6	tasks	task	NOUN
ajst-20810	74	7	,	,	PUNCT
ajst-20810	74	8	in	in	ADP
ajst-20810	74	9	order	order	NOUN
ajst-20810	74	10	to	to	PART
ajst-20810	74	11	solve	solve	VERB
ajst-20810	74	12	partial	partial	ADJ
ajst-20810	74	13	differential	differential	NOUN
ajst-20810	74	14	equations	equation	NOUN
ajst-20810	74	15	(	(	PUNCT
ajst-20810	74	16	pdes	pde	NOUN
ajst-20810	74	17	)	)	PUNCT
ajst-20810	74	18	,	,	PUNCT
ajst-20810	74	19	scientists	scientist	NOUN
ajst-20810	74	20	divide	divide	VERB
ajst-20810	74	21	the	the	DET
ajst-20810	74	22	system	system	NOUN
ajst-20810	74	23	into	into	ADP
ajst-20810	74	24	multiple	multiple	ADJ
ajst-20810	74	25	sub	sub	NOUN
ajst-20810	74	26	-	-	NOUN
ajst-20810	74	27	tasks	task	NOUN
ajst-20810	74	28	on	on	ADP
ajst-20810	74	29	multiple	multiple	ADJ
ajst-20810	74	30	scales	scale	NOUN
ajst-20810	74	31	by	by	ADP
ajst-20810	74	32	means	mean	NOUN
ajst-20810	74	33	of	of	ADP
ajst-20810	74	34	multiple	multiple	ADJ
ajst-20810	74	35	meshes	mesh	NOUN
ajst-20810	74	36	.	.	PUNCT
ajst-20810	75	1	or	or	CCONJ
ajst-20810	75	2	rather	rather	ADV
ajst-20810	75	3	,	,	PUNCT
ajst-20810	75	4	the	the	DET
ajst-20810	75	5	task	task	NOUN
ajst-20810	75	6	is	be	AUX
ajst-20810	75	7	refined	refine	VERB
ajst-20810	75	8	step	step	NOUN
ajst-20810	75	9	by	by	ADP
ajst-20810	75	10	step	step	NOUN
ajst-20810	75	11	to	to	PART
ajst-20810	75	12	create	create	VERB
ajst-20810	75	13	a	a	DET
ajst-20810	75	14	different	different	ADJ
ajst-20810	75	15	sub	sub	NOUN
ajst-20810	75	16	-	-	NOUN
ajst-20810	75	17	task	task	NOUN
ajst-20810	75	18	,	,	PUNCT
ajst-20810	75	19	each	each	PRON
ajst-20810	75	20	dealing	deal	VERB
ajst-20810	75	21	with	with	ADP
ajst-20810	75	22	residual	residual	ADJ
ajst-20810	75	23	solutions	solution	NOUN
ajst-20810	75	24	at	at	ADP
ajst-20810	75	25	different	different	ADJ
ajst-20810	75	26	scales	scale	NOUN
ajst-20810	75	27	.	.	PUNCT
ajst-20810	76	1	compared	compare	VERB
ajst-20810	76	2	to	to	ADP
ajst-20810	76	3	normal	normal	ADJ
ajst-20810	76	4	solvers	solver	NOUN
ajst-20810	76	5	,	,	PUNCT
ajst-20810	76	6	these	these	DET
ajst-20810	76	7	solutions	solution	NOUN
ajst-20810	76	8	that	that	PRON
ajst-20810	76	9	employ	employ	VERB
ajst-20810	76	10	the	the	DET
ajst-20810	76	11	residual	residual	ADJ
ajst-20810	76	12	property	property	NOUN
ajst-20810	76	13	converge	converge	NOUN
ajst-20810	76	14	faster	fast	ADV
ajst-20810	76	15	in	in	ADP
ajst-20810	76	16	processing	process	VERB
ajst-20810	76	17	the	the	DET
ajst-20810	76	18	image	image	NOUN
ajst-20810	76	19	,	,	PUNCT
ajst-20810	76	20	which	which	PRON
ajst-20810	76	21	proves	prove	VERB
ajst-20810	76	22	that	that	SCONJ
ajst-20810	76	23	applying	apply	VERB
ajst-20810	76	24	more	more	ADV
ajst-20810	76	25	advanced	advanced	ADJ
ajst-20810	76	26	concepts	concept	NOUN
ajst-20810	76	27	and	and	CCONJ
ajst-20810	76	28	using	use	VERB
ajst-20810	76	29	a	a	DET
ajst-20810	76	30	better	well	ADJ
ajst-20810	76	31	processing	processing	NOUN
ajst-20810	76	32	method	method	NOUN
ajst-20810	76	33	can	can	AUX
ajst-20810	76	34	enhance	enhance	VERB
ajst-20810	76	35	the	the	DET
ajst-20810	76	36	optimisation	optimisation	NOUN
ajst-20810	76	37	well	well	ADV
ajst-20810	76	38	.	.	PUNCT
ajst-20810	77	1	while	while	SCONJ
ajst-20810	77	2	conducting	conduct	VERB
ajst-20810	77	3	research	research	NOUN
ajst-20810	77	4	on	on	ADP
ajst-20810	77	5	residual	residual	ADJ
ajst-20810	77	6	connectivity	connectivity	NOUN
ajst-20810	77	7	,	,	PUNCT
ajst-20810	77	8	"	"	PUNCT
ajst-20810	77	9	highway	highway	NOUN
ajst-20810	77	10	network[12	network[12	NOUN
ajst-20810	77	11	]	]	PUNCT
ajst-20810	77	12	"	"	PUNCT
ajst-20810	77	13	provides	provide	VERB
ajst-20810	77	14	shortcut	shortcut	NOUN
ajst-20810	77	15	connections	connection	NOUN
ajst-20810	77	16	with	with	ADP
ajst-20810	77	17	gating	gate	VERB
ajst-20810	77	18	features	feature	NOUN
ajst-20810	77	19	.	.	PUNCT
ajst-20810	78	1	this	this	DET
ajst-20810	78	2	kind	kind	NOUN
ajst-20810	78	3	of	of	ADP
ajst-20810	78	4	shortcut	shortcut	NOUN
ajst-20810	78	5	connections	connection	NOUN
ajst-20810	78	6	can	can	AUX
ajst-20810	78	7	somehow	somehow	ADV
ajst-20810	78	8	be	be	AUX
ajst-20810	78	9	beautiful	beautiful	ADJ
ajst-20810	78	10	with	with	ADP
ajst-20810	78	11	the	the	DET
ajst-20810	78	12	idea	idea	NOUN
ajst-20810	78	13	of	of	ADP
ajst-20810	78	14	residual	residual	ADJ
ajst-20810	78	15	connections	connection	NOUN
ajst-20810	78	16	,	,	PUNCT
ajst-20810	78	17	however	however	ADV
ajst-20810	78	18	,	,	PUNCT
ajst-20810	78	19	these	these	DET
ajst-20810	78	20	gating	gate	VERB
ajst-20810	78	21	mechanisms	mechanism	NOUN
ajst-20810	78	22	are	be	AUX
ajst-20810	78	23	more	more	ADJ
ajst-20810	78	24	data	datum	NOUN
ajst-20810	78	25	dependent	dependent	ADJ
ajst-20810	78	26	and	and	CCONJ
ajst-20810	78	27	they	they	PRON
ajst-20810	78	28	also	also	ADV
ajst-20810	78	29	have	have	VERB
ajst-20810	78	30	extra	extra	ADJ
ajst-20810	78	31	parameters	parameter	NOUN
ajst-20810	78	32	,	,	PUNCT
ajst-20810	78	33	this	this	DET
ajst-20810	78	34	drawback	drawback	NOUN
ajst-20810	78	35	increases	increase	VERB
ajst-20810	78	36	the	the	DET
ajst-20810	78	37	computational	computational	ADJ
ajst-20810	78	38	effort	effort	NOUN
ajst-20810	78	39	and	and	CCONJ
ajst-20810	78	40	training	training	NOUN
ajst-20810	78	41	of	of	ADP
ajst-20810	78	42	the	the	DET
ajst-20810	78	43	network	network	NOUN
ajst-20810	78	44	.	.	PUNCT
ajst-20810	79	1	on	on	ADP
ajst-20810	79	2	the	the	DET
ajst-20810	79	3	other	other	ADJ
ajst-20810	79	4	hand	hand	NOUN
ajst-20810	79	5	,	,	PUNCT
ajst-20810	79	6	when	when	SCONJ
ajst-20810	79	7	the	the	DET
ajst-20810	79	8	gating	gate	VERB
ajst-20810	79	9	mechanisms	mechanism	NOUN
ajst-20810	79	10	are	be	AUX
ajst-20810	79	11	turned	turn	VERB
ajst-20810	79	12	off	off	ADP
ajst-20810	79	13	,	,	PUNCT
ajst-20810	79	14	their	their	PRON
ajst-20810	79	15	network	network	NOUN
ajst-20810	79	16	representation	representation	NOUN
ajst-20810	79	17	has	have	VERB
ajst-20810	79	18	no	no	DET
ajst-20810	79	19	advantage	advantage	NOUN
ajst-20810	79	20	as	as	ADP
ajst-20810	79	21	in	in	ADP
ajst-20810	79	22	the	the	DET
ajst-20810	79	23	case	case	NOUN
ajst-20810	79	24	of	of	ADP
ajst-20810	79	25	nonresidual	nonresidual	ADJ
ajst-20810	79	26	networks	network	NOUN
ajst-20810	79	27	.	.	PUNCT
ajst-20810	80	1	unlike	unlike	ADP
ajst-20810	80	2	this	this	DET
ajst-20810	80	3	network	network	NOUN
ajst-20810	80	4	,	,	PUNCT
ajst-20810	80	5	the	the	DET
ajst-20810	80	6	layer	layer	NOUN
ajst-20810	80	7	-	-	PUNCT
ajst-20810	80	8	hopping	hop	VERB
ajst-20810	80	9	connected	connect	VERB
ajst-20810	80	10	constant	constant	ADJ
ajst-20810	80	11	mapping	mapping	NOUN
ajst-20810	80	12	module	module	NOUN
ajst-20810	80	13	in	in	ADP
ajst-20810	80	14	the	the	DET
ajst-20810	80	15	residual	residual	ADJ
ajst-20810	80	16	network	network	NOUN
ajst-20810	80	17	does	do	AUX
ajst-20810	80	18	not	not	PART
ajst-20810	80	19	introduce	introduce	VERB
ajst-20810	80	20	additional	additional	ADJ
ajst-20810	80	21	training	training	NOUN
ajst-20810	80	22	parameters	parameter	NOUN
ajst-20810	80	23	or	or	CCONJ
ajst-20810	80	24	hyperparameters	hyperparameter	NOUN
ajst-20810	80	25	and	and	CCONJ
ajst-20810	80	26	is	be	AUX
ajst-20810	80	27	committed	commit	VERB
ajst-20810	80	28	to	to	ADP
ajst-20810	80	29	learning	learn	VERB
ajst-20810	80	30	the	the	DET
ajst-20810	80	31	residual	residual	ADJ
ajst-20810	80	32	function	function	NOUN
ajst-20810	80	33	this	this	DET
ajst-20810	80	34	operation	operation	NOUN
ajst-20810	80	35	does	do	AUX
ajst-20810	80	36	not	not	PART
ajst-20810	80	37	ignore	ignore	VERB
ajst-20810	80	38	the	the	DET
ajst-20810	80	39	learning	learning	NOUN
ajst-20810	80	40	of	of	ADP
ajst-20810	80	41	the	the	DET
ajst-20810	80	42	constant	constant	ADJ
ajst-20810	80	43	mapping	mapping	NOUN
ajst-20810	80	44	and	and	CCONJ
ajst-20810	80	45	the	the	DET
ajst-20810	80	46	highway	highway	NOUN
ajst-20810	80	47	network	network	NOUN
ajst-20810	80	48	does	do	AUX
ajst-20810	80	49	not	not	PART
ajst-20810	80	50	have	have	VERB
ajst-20810	80	51	the	the	DET
ajst-20810	80	52	advantage	advantage	NOUN
ajst-20810	80	53	of	of	ADP
ajst-20810	80	54	performing	perform	VERB
ajst-20810	80	55	as	as	ADV
ajst-20810	80	56	well	well	ADV
ajst-20810	80	57	as	as	ADP
ajst-20810	80	58	the	the	DET
ajst-20810	80	59	residual	residual	ADJ
ajst-20810	80	60	network	network	NOUN
ajst-20810	80	61	in	in	ADP
ajst-20810	80	62	the	the	DET
ajst-20810	80	63	deeper	deep	ADJ
ajst-20810	80	64	network	network	NOUN
ajst-20810	80	65	.	.	PUNCT
ajst-20810	81	1	4	4	X
ajst-20810	81	2	.	.	X
ajst-20810	81	3	shufflenet	shufflenet	NOUN
ajst-20810	81	4	-	-	PUNCT
ajst-20810	81	5	based	base	VERB
ajst-20810	81	6	image	image	NOUN
ajst-20810	81	7	classification	classification	NOUN
ajst-20810	81	8	4.1	4.1	NUM
ajst-20810	81	9	.	.	PUNCT
ajst-20810	82	1	group	group	NOUN
ajst-20810	82	2	convolution	convolution	NOUN
ajst-20810	82	3	under	under	ADP
ajst-20810	82	4	channel	channel	NOUN
ajst-20810	82	5	substitution	substitution	NOUN
ajst-20810	82	6	modern	modern	ADJ
ajst-20810	82	7	convolutional	convolutional	ADJ
ajst-20810	82	8	neural	neural	ADJ
ajst-20810	82	9	networks	network	NOUN
ajst-20810	82	10	are	be	AUX
ajst-20810	82	11	generally	generally	ADV
ajst-20810	82	12	made	make	VERB
ajst-20810	82	13	up	up	ADP
ajst-20810	82	14	of	of	ADP
ajst-20810	82	15	network	network	NOUN
ajst-20810	82	16	layers	layer	NOUN
ajst-20810	82	17	with	with	ADP
ajst-20810	82	18	the	the	DET
ajst-20810	82	19	same	same	ADJ
ajst-20810	82	20	structure	structure	NOUN
ajst-20810	82	21	stacked	stack	VERB
ajst-20810	82	22	in	in	ADP
ajst-20810	82	23	different	different	ADJ
ajst-20810	82	24	ways	way	NOUN
ajst-20810	82	25	.	.	PUNCT
ajst-20810	83	1	in	in	ADP
ajst-20810	83	2	the	the	DET
ajst-20810	83	3	xception	xception	NOUN
ajst-20810	83	4	and	and	CCONJ
ajst-20810	83	5	resnext	resnext	NOUN
ajst-20810	83	6	models	model	NOUN
ajst-20810	83	7	,	,	PUNCT
ajst-20810	83	8	the	the	DET
ajst-20810	83	9	1	1	NUM
ajst-20810	83	10	1	1	NUM
ajst-20810	83	11	´	´	NOUN
ajst-20810	83	12	convolution	convolution	NOUN
ajst-20810	83	13	and	and	CCONJ
ajst-20810	83	14	deep	deep	ADJ
ajst-20810	83	15	separable	separable	ADJ
ajst-20810	83	16	convolution	convolution	NOUN
ajst-20810	83	17	were	be	AUX
ajst-20810	83	18	introduced	introduce	VERB
ajst-20810	83	19	into	into	ADP
ajst-20810	83	20	the	the	DET
ajst-20810	83	21	models	model	NOUN
ajst-20810	83	22	in	in	ADP
ajst-20810	83	23	order	order	NOUN
ajst-20810	83	24	to	to	PART
ajst-20810	83	25	strike	strike	VERB
ajst-20810	83	26	an	an	DET
ajst-20810	83	27	effective	effective	ADJ
ajst-20810	83	28	balance	balance	NOUN
ajst-20810	83	29	between	between	ADP
ajst-20810	83	30	the	the	DET
ajst-20810	83	31	feature	feature	NOUN
ajst-20810	83	32	extraction	extraction	NOUN
ajst-20810	83	33	capability	capability	NOUN
ajst-20810	83	34	of	of	ADP
ajst-20810	83	35	the	the	DET
ajst-20810	83	36	model	model	NOUN
ajst-20810	83	37	and	and	CCONJ
ajst-20810	83	38	the	the	DET
ajst-20810	83	39	computational	computational	ADJ
ajst-20810	83	40	complexity	complexity	NOUN
ajst-20810	83	41	.	.	PUNCT
ajst-20810	84	1	however	however	ADV
ajst-20810	84	2	,	,	PUNCT
ajst-20810	84	3	the	the	DET
ajst-20810	84	4	1	1	NUM
ajst-20810	84	5	1	1	NUM
ajst-20810	84	6	´	´	NOUN
ajst-20810	84	7	convolution	convolution	NOUN
ajst-20810	84	8	contains	contain	VERB
ajst-20810	84	9	significant	significant	ADJ
ajst-20810	84	10	computational	computational	ADJ
ajst-20810	84	11	complexity	complexity	NOUN
ajst-20810	84	12	and	and	CCONJ
ajst-20810	84	13	parameters	parameter	NOUN
ajst-20810	84	14	in	in	ADP
ajst-20810	84	15	both	both	DET
ajst-20810	84	16	models	model	NOUN
ajst-20810	84	17	.	.	PUNCT
ajst-20810	85	1	for	for	ADP
ajst-20810	85	2	example	example	NOUN
ajst-20810	85	3	,	,	PUNCT
ajst-20810	85	4	in	in	ADP
ajst-20810	85	5	resnext	resnext	NOUN
ajst-20810	85	6	,	,	PUNCT
ajst-20810	85	7	the	the	DET
ajst-20810	85	8	only	only	ADJ
ajst-20810	85	9	convolutional	convolutional	ADJ
ajst-20810	85	10	layer	layer	NOUN
ajst-20810	85	11	that	that	PRON
ajst-20810	85	12	employs	employ	VERB
ajst-20810	85	13	group	group	NOUN
ajst-20810	85	14	convolution	convolution	NOUN
ajst-20810	85	15	is	be	AUX
ajst-20810	85	16	3	3	NUM
ajst-20810	85	17	3	3	NUM
ajst-20810	85	18	´	´	NOUN
ajst-20810	85	19	,	,	PUNCT
ajst-20810	85	20	and	and	CCONJ
ajst-20810	85	21	the	the	DET
ajst-20810	85	22	other	other	ADJ
ajst-20810	85	23	layers	layer	NOUN
ajst-20810	85	24	are	be	AUX
ajst-20810	85	25	not	not	PART
ajst-20810	85	26	equipped	equip	VERB
ajst-20810	85	27	for	for	ADP
ajst-20810	85	28	use	use	NOUN
ajst-20810	85	29	.	.	PUNCT
ajst-20810	86	1	so	so	ADV
ajst-20810	86	2	in	in	ADP
ajst-20810	86	3	each	each	DET
ajst-20810	86	4	residual	residual	ADJ
ajst-20810	86	5	structure	structure	NOUN
ajst-20810	86	6	in	in	ADP
ajst-20810	86	7	the	the	DET
ajst-20810	86	8	resnext	resnext	ADJ
ajst-20810	86	9	model	model	NOUN
ajst-20810	86	10	,	,	PUNCT
ajst-20810	86	11	the	the	DET
ajst-20810	86	12	1	1	NUM
ajst-20810	86	13	1	1	NUM
ajst-20810	86	14	´	´	NOUN
ajst-20810	86	15	convolution	convolution	NOUN
ajst-20810	86	16	has	have	VERB
ajst-20810	86	17	93.4	93.4	NUM
ajst-20810	86	18	%	%	NOUN
ajst-20810	86	19	product	product	NOUN
ajst-20810	86	20	.	.	PUNCT
ajst-20810	87	1	in	in	ADP
ajst-20810	87	2	a	a	DET
ajst-20810	87	3	small	small	ADJ
ajst-20810	87	4	network	network	NOUN
ajst-20810	87	5	,	,	PUNCT
ajst-20810	87	6	the	the	DET
ajst-20810	87	7	higher	high	ADJ
ajst-20810	87	8	complexity	complexity	NOUN
ajst-20810	87	9	of	of	ADP
ajst-20810	87	10	the	the	DET
ajst-20810	87	11	1	1	NUM
ajst-20810	87	12	1	1	NUM
ajst-20810	87	13	´	´	NOUN
ajst-20810	87	14	convolution	convolution	NOUN
ajst-20810	87	15	makes	make	VERB
ajst-20810	87	16	the	the	DET
ajst-20810	87	17	number	number	NOUN
ajst-20810	87	18	of	of	ADP
ajst-20810	87	19	feature	feature	NOUN
ajst-20810	87	20	channels	channel	NOUN
ajst-20810	87	21	limited	limit	VERB
ajst-20810	87	22	and	and	CCONJ
ajst-20810	87	23	can	can	AUX
ajst-20810	87	24	not	not	PART
ajst-20810	87	25	be	be	AUX
ajst-20810	87	26	stacked	stack	VERB
ajst-20810	87	27	,	,	PUNCT
ajst-20810	87	28	which	which	PRON
ajst-20810	87	29	will	will	AUX
ajst-20810	87	30	lead	lead	VERB
ajst-20810	87	31	to	to	ADP
ajst-20810	87	32	the	the	DET
ajst-20810	87	33	performance	performance	NOUN
ajst-20810	87	34	of	of	ADP
ajst-20810	87	35	the	the	DET
ajst-20810	87	36	model	model	NOUN
ajst-20810	87	37	.	.	PUNCT
ajst-20810	88	1	therefore	therefore	ADV
ajst-20810	88	2	how	how	SCONJ
ajst-20810	88	3	to	to	PART
ajst-20810	88	4	balance	balance	VERB
ajst-20810	88	5	the	the	DET
ajst-20810	88	6	accuracy	accuracy	NOUN
ajst-20810	88	7	and	and	CCONJ
ajst-20810	88	8	computational	computational	ADJ
ajst-20810	88	9	complexity	complexity	NOUN
ajst-20810	88	10	of	of	ADP
ajst-20810	88	11	the	the	DET
ajst-20810	88	12	model	model	NOUN
ajst-20810	88	13	becomes	become	VERB
ajst-20810	88	14	the	the	DET
ajst-20810	88	15	problem	problem	NOUN
ajst-20810	88	16	addressed	address	VERB
ajst-20810	88	17	by	by	ADP
ajst-20810	88	18	shumenet	shumenet	NOUN
ajst-20810	88	19	.	.	PUNCT
ajst-20810	89	1	figure	figure	VERB
ajst-20810	89	2	3	3	NUM
ajst-20810	89	3	.	.	NOUN
ajst-20810	89	4	channel	channel	NOUN
ajst-20810	89	5	disruption	disruption	NOUN
ajst-20810	89	6	with	with	ADP
ajst-20810	89	7	two	two	NUM
ajst-20810	89	8	stacked	stack	VERB
ajst-20810	89	9	group	group	NOUN
ajst-20810	89	10	convolutions	convolution	NOUN
ajst-20810	89	11	.	.	PUNCT
ajst-20810	90	1	(	(	PUNCT
ajst-20810	90	2	a	a	X
ajst-20810	90	3	)	)	PUNCT
ajst-20810	90	4	two	two	NUM
ajst-20810	90	5	stacked	stack	VERB
ajst-20810	90	6	group	group	NOUN
ajst-20810	90	7	convolution	convolution	NOUN
ajst-20810	90	8	layers	layer	NOUN
ajst-20810	90	9	;	;	PUNCT
ajst-20810	90	10	(	(	PUNCT
ajst-20810	90	11	b	b	X
ajst-20810	90	12	)	)	PUNCT
ajst-20810	90	13	stacked	stack	VERB
ajst-20810	90	14	group	group	NOUN
ajst-20810	90	15	convolution	convolution	NOUN
ajst-20810	90	16	with	with	ADP
ajst-20810	90	17	channel	channel	NOUN
ajst-20810	90	18	disruption	disruption	NOUN
ajst-20810	90	19	;	;	PUNCT
ajst-20810	90	20	(	(	PUNCT
ajst-20810	90	21	c	c	X
ajst-20810	90	22	)	)	PUNCT
ajst-20810	90	23	group	group	NOUN
ajst-20810	90	24	convolution	convolution	NOUN
ajst-20810	90	25	after	after	ADP
ajst-20810	90	26	channel	channel	NOUN
ajst-20810	90	27	disruption	disruption	NOUN
ajst-20810	90	28	in	in	ADP
ajst-20810	90	29	order	order	NOUN
ajst-20810	90	30	to	to	PART
ajst-20810	90	31	do	do	AUX
ajst-20810	90	32	this	this	PRON
ajst-20810	90	33	while	while	SCONJ
ajst-20810	90	34	satisfying	satisfy	VERB
ajst-20810	90	35	sufficient	sufficient	ADJ
ajst-20810	90	36	accuracy	accuracy	NOUN
ajst-20810	90	37	and	and	CCONJ
ajst-20810	90	38	complexity	complexity	NOUN
ajst-20810	91	1	,	,	PUNCT
ajst-20810	91	2	some	some	DET
ajst-20810	91	3	ideas	idea	NOUN
ajst-20810	91	4	have	have	AUX
ajst-20810	91	5	been	be	AUX
ajst-20810	91	6	proposed	propose	VERB
ajst-20810	91	7	in	in	ADP
ajst-20810	91	8	shumenet	shumenet	NOUN
ajst-20810	91	9	.	.	PUNCT
ajst-20810	92	1	sparse	sparse	ADJ
ajst-20810	92	2	channel	channel	NOUN
ajst-20810	92	3	connections	connection	NOUN
ajst-20810	92	4	are	be	AUX
ajst-20810	92	5	applied	apply	VERB
ajst-20810	92	6	in	in	ADP
ajst-20810	92	7	the	the	DET
ajst-20810	92	8	model	model	NOUN
ajst-20810	92	9	,	,	PUNCT
ajst-20810	92	10	for	for	ADP
ajst-20810	92	11	example	example	NOUN
ajst-20810	92	12	by	by	ADP
ajst-20810	92	13	performing	perform	VERB
ajst-20810	92	14	group	group	NOUN
ajst-20810	92	15	convolution	convolution	NOUN
ajst-20810	92	16	on	on	ADP
ajst-20810	92	17	the	the	DET
ajst-20810	92	18	1	1	NUM
ajst-20810	92	19	1	1	NUM
ajst-20810	92	20	´	´	NOUN
ajst-20810	92	21	layers	layer	NOUN
ajst-20810	92	22	.	.	PUNCT
ajst-20810	93	1	previously	previously	ADV
ajst-20810	93	2	,	,	PUNCT
ajst-20810	93	3	the	the	DET
ajst-20810	93	4	convolution	convolution	NOUN
ajst-20810	93	5	of	of	ADP
ajst-20810	93	6	each	each	DET
ajst-20810	93	7	group	group	NOUN
ajst-20810	93	8	was	be	AUX
ajst-20810	93	9	run	run	VERB
ajst-20810	93	10	only	only	ADV
ajst-20810	93	11	on	on	ADP
ajst-20810	93	12	the	the	DET
ajst-20810	93	13	corresponding	correspond	VERB
ajst-20810	93	14	channel	channel	NOUN
ajst-20810	93	15	with	with	ADP
ajst-20810	93	16	no	no	DET
ajst-20810	93	17	additional	additional	ADJ
ajst-20810	93	18	number	number	NOUN
ajst-20810	93	19	of	of	ADP
ajst-20810	93	20	parameters	parameter	NOUN
ajst-20810	93	21	,	,	PUNCT
ajst-20810	93	22	and	and	CCONJ
ajst-20810	93	23	the	the	DET
ajst-20810	93	24	computational	computational	ADJ
ajst-20810	93	25	complexity	complexity	NOUN
ajst-20810	93	26	of	of	ADP
ajst-20810	93	27	the	the	DET
ajst-20810	93	28	model	model	NOUN
ajst-20810	93	29	decreased	decrease	VERB
ajst-20810	93	30	.	.	PUNCT
ajst-20810	94	1	however	however	ADV
ajst-20810	94	2	,	,	PUNCT
ajst-20810	94	3	setting	set	VERB
ajst-20810	94	4	up	up	ADP
ajst-20810	94	5	multiple	multiple	ADJ
ajst-20810	94	6	group	group	NOUN
ajst-20810	94	7	convolutions	convolution	NOUN
ajst-20810	94	8	181	181	NUM
ajst-20810	94	9	stacked	stack	VERB
ajst-20810	94	10	together	together	ADV
ajst-20810	94	11	for	for	ADP
ajst-20810	94	12	model	model	NOUN
ajst-20810	94	13	design	design	NOUN
ajst-20810	94	14	purposes	purpose	NOUN
ajst-20810	94	15	inevitably	inevitably	ADV
ajst-20810	94	16	creates	create	VERB
ajst-20810	94	17	an	an	DET
ajst-20810	94	18	important	important	ADJ
ajst-20810	94	19	problem	problem	NOUN
ajst-20810	94	20	:	:	PUNCT
ajst-20810	94	21	a	a	DET
ajst-20810	94	22	channel	channel	NOUN
ajst-20810	94	23	in	in	ADP
ajst-20810	94	24	the	the	DET
ajst-20810	94	25	middle	middle	ADJ
ajst-20810	94	26	layer	layer	NOUN
ajst-20810	94	27	of	of	ADP
ajst-20810	94	28	the	the	DET
ajst-20810	94	29	network	network	NOUN
ajst-20810	94	30	is	be	AUX
ajst-20810	94	31	only	only	ADV
ajst-20810	94	32	correlated	correlate	VERB
ajst-20810	94	33	with	with	ADP
ajst-20810	94	34	the	the	DET
ajst-20810	94	35	input	input	NOUN
ajst-20810	94	36	channel	channel	NOUN
ajst-20810	94	37	corresponding	correspond	VERB
ajst-20810	94	38	to	to	ADP
ajst-20810	94	39	that	that	DET
ajst-20810	94	40	channel	channel	NOUN
ajst-20810	94	41	,	,	PUNCT
ajst-20810	94	42	and	and	CCONJ
ajst-20810	94	43	the	the	DET
ajst-20810	94	44	output	output	NOUN
ajst-20810	94	45	value	value	NOUN
ajst-20810	94	46	of	of	ADP
ajst-20810	94	47	that	that	DET
ajst-20810	94	48	channel	channel	NOUN
ajst-20810	94	49	is	be	AUX
ajst-20810	94	50	then	then	ADV
ajst-20810	94	51	only	only	ADV
ajst-20810	94	52	correlated	correlate	VERB
ajst-20810	94	53	with	with	ADP
ajst-20810	94	54	its	its	PRON
ajst-20810	94	55	corresponding	corresponding	ADJ
ajst-20810	94	56	input	input	NOUN
ajst-20810	94	57	channel	channel	NOUN
ajst-20810	94	58	,	,	PUNCT
ajst-20810	94	59	and	and	CCONJ
ajst-20810	94	60	not	not	PART
ajst-20810	94	61	with	with	ADP
ajst-20810	94	62	the	the	DET
ajst-20810	94	63	channels	channel	NOUN
ajst-20810	94	64	of	of	ADP
ajst-20810	94	65	the	the	DET
ajst-20810	94	66	other	other	ADJ
ajst-20810	94	67	groups	group	NOUN
ajst-20810	94	68	.	.	PUNCT
ajst-20810	95	1	the	the	DET
ajst-20810	95	2	case	case	NOUN
ajst-20810	95	3	of	of	ADP
ajst-20810	95	4	two	two	NUM
ajst-20810	95	5	stacked	stack	VERB
ajst-20810	95	6	group	group	NOUN
ajst-20810	95	7	convolutional	convolutional	ADJ
ajst-20810	95	8	layers	layer	NOUN
ajst-20810	95	9	is	be	AUX
ajst-20810	95	10	shown	show	VERB
ajst-20810	95	11	in	in	ADP
ajst-20810	95	12	fig	fig	NOUN
ajst-20810	95	13	.	.	PUNCT
ajst-20810	96	1	3(a	3(a	NUM
ajst-20810	96	2	)	)	PUNCT
ajst-20810	96	3	.	.	PUNCT
ajst-20810	97	1	it	it	PRON
ajst-20810	97	2	is	be	AUX
ajst-20810	97	3	obvious	obvious	ADJ
ajst-20810	97	4	that	that	SCONJ
ajst-20810	97	5	the	the	DET
ajst-20810	97	6	output	output	NOUN
ajst-20810	97	7	features	feature	VERB
ajst-20810	97	8	from	from	ADP
ajst-20810	97	9	a	a	DET
ajst-20810	97	10	given	give	VERB
ajst-20810	97	11	group	group	NOUN
ajst-20810	97	12	only	only	ADV
ajst-20810	97	13	interact	interact	VERB
ajst-20810	97	14	informatively	informatively	ADV
ajst-20810	97	15	with	with	ADP
ajst-20810	97	16	the	the	DET
ajst-20810	97	17	features	feature	NOUN
ajst-20810	97	18	within	within	ADP
ajst-20810	97	19	its	its	PRON
ajst-20810	97	20	corresponding	corresponding	ADJ
ajst-20810	97	21	input	input	NOUN
ajst-20810	97	22	group	group	NOUN
ajst-20810	97	23	,	,	PUNCT
ajst-20810	97	24	and	and	CCONJ
ajst-20810	97	25	are	be	AUX
ajst-20810	97	26	completely	completely	ADV
ajst-20810	97	27	disconnected	disconnected	ADJ
ajst-20810	97	28	from	from	ADP
ajst-20810	97	29	the	the	DET
ajst-20810	97	30	features	feature	NOUN
ajst-20810	97	31	between	between	ADP
ajst-20810	97	32	the	the	DET
ajst-20810	97	33	groups	group	NOUN
ajst-20810	97	34	.	.	PUNCT
ajst-20810	98	1	as	as	ADP
ajst-20810	98	2	a	a	DET
ajst-20810	98	3	result	result	NOUN
ajst-20810	98	4	,	,	PUNCT
ajst-20810	98	5	the	the	DET
ajst-20810	98	6	grouping	group	VERB
ajst-20810	98	7	convolution	convolution	NOUN
ajst-20810	98	8	greatly	greatly	ADV
ajst-20810	98	9	hinders	hinder	VERB
ajst-20810	98	10	the	the	DET
ajst-20810	98	11	flow	flow	NOUN
ajst-20810	98	12	of	of	ADP
ajst-20810	98	13	information	information	NOUN
ajst-20810	98	14	and	and	CCONJ
ajst-20810	98	15	reorganisation	reorganisation	NOUN
ajst-20810	98	16	between	between	ADP
ajst-20810	98	17	the	the	DET
ajst-20810	98	18	individual	individual	ADJ
ajst-20810	98	19	groupings	grouping	NOUN
ajst-20810	98	20	of	of	ADP
ajst-20810	98	21	the	the	DET
ajst-20810	98	22	channel	channel	NOUN
ajst-20810	98	23	,	,	PUNCT
ajst-20810	98	24	largely	largely	ADV
ajst-20810	98	25	weakening	weaken	VERB
ajst-20810	98	26	the	the	DET
ajst-20810	98	27	expressive	expressive	ADJ
ajst-20810	98	28	power	power	NOUN
ajst-20810	98	29	of	of	ADP
ajst-20810	98	30	the	the	DET
ajst-20810	98	31	network	network	NOUN
ajst-20810	98	32	.	.	PUNCT
ajst-20810	99	1	assuming	assume	VERB
ajst-20810	99	2	that	that	SCONJ
ajst-20810	99	3	group	group	NOUN
ajst-20810	99	4	convolution	convolution	NOUN
ajst-20810	99	5	can	can	AUX
ajst-20810	99	6	obtain	obtain	VERB
ajst-20810	99	7	data	datum	NOUN
ajst-20810	99	8	from	from	ADP
ajst-20810	99	9	different	different	ADJ
ajst-20810	99	10	input	input	NOUN
ajst-20810	99	11	groups	group	NOUN
ajst-20810	99	12	(	(	PUNCT
ajst-20810	99	13	as	as	SCONJ
ajst-20810	99	14	shown	show	VERB
ajst-20810	99	15	in	in	ADP
ajst-20810	99	16	fig	fig	NOUN
ajst-20810	99	17	.	.	PUNCT
ajst-20810	100	1	3(b	3(b	NUM
ajst-20810	100	2	)	)	PUNCT
ajst-20810	100	3	)	)	PUNCT
ajst-20810	100	4	,	,	PUNCT
ajst-20810	100	5	the	the	DET
ajst-20810	100	6	information	information	NOUN
ajst-20810	100	7	between	between	ADP
ajst-20810	100	8	the	the	DET
ajst-20810	100	9	input	input	NOUN
ajst-20810	100	10	and	and	CCONJ
ajst-20810	100	11	output	output	NOUN
ajst-20810	100	12	features	feature	NOUN
ajst-20810	100	13	of	of	ADP
ajst-20810	100	14	different	different	ADJ
ajst-20810	100	15	groups	group	NOUN
ajst-20810	100	16	in	in	ADP
ajst-20810	100	17	the	the	DET
ajst-20810	100	18	model	model	NOUN
ajst-20810	100	19	will	will	AUX
ajst-20810	100	20	circulate	circulate	VERB
ajst-20810	100	21	with	with	ADP
ajst-20810	100	22	each	each	DET
ajst-20810	100	23	other	other	ADJ
ajst-20810	100	24	in	in	ADP
ajst-20810	100	25	time	time	NOUN
ajst-20810	100	26	.	.	PUNCT
ajst-20810	101	1	that	that	PRON
ajst-20810	101	2	is	be	AUX
ajst-20810	101	3	to	to	PART
ajst-20810	101	4	say	say	VERB
ajst-20810	101	5	,	,	PUNCT
ajst-20810	101	6	for	for	ADP
ajst-20810	101	7	the	the	DET
ajst-20810	101	8	feature	feature	NOUN
ajst-20810	101	9	maps	map	NOUN
ajst-20810	101	10	generated	generate	VERB
ajst-20810	101	11	in	in	ADP
ajst-20810	101	12	the	the	DET
ajst-20810	101	13	previous	previous	ADJ
ajst-20810	101	14	set	set	NOUN
ajst-20810	101	15	of	of	ADP
ajst-20810	101	16	layers	layer	NOUN
ajst-20810	101	17	,	,	PUNCT
ajst-20810	101	18	the	the	DET
ajst-20810	101	19	channels	channel	NOUN
ajst-20810	101	20	in	in	ADP
ajst-20810	101	21	each	each	DET
ajst-20810	101	22	group	group	NOUN
ajst-20810	101	23	are	be	AUX
ajst-20810	101	24	first	first	ADV
ajst-20810	101	25	divided	divide	VERB
ajst-20810	101	26	into	into	ADP
ajst-20810	101	27	several	several	ADJ
ajst-20810	101	28	subgroups	subgroup	NOUN
ajst-20810	101	29	,	,	PUNCT
ajst-20810	101	30	and	and	CCONJ
ajst-20810	101	31	then	then	ADV
ajst-20810	101	32	the	the	DET
ajst-20810	101	33	channel	channel	NOUN
ajst-20810	101	34	feature	feature	NOUN
ajst-20810	101	35	information	information	NOUN
ajst-20810	101	36	of	of	ADP
ajst-20810	101	37	different	different	ADJ
ajst-20810	101	38	subgroups	subgroup	NOUN
ajst-20810	101	39	in	in	ADP
ajst-20810	101	40	the	the	DET
ajst-20810	101	41	previous	previous	ADJ
ajst-20810	101	42	layer	layer	NOUN
ajst-20810	101	43	is	be	AUX
ajst-20810	101	44	provided	provide	VERB
ajst-20810	101	45	to	to	ADP
ajst-20810	101	46	each	each	DET
ajst-20810	101	47	group	group	NOUN
ajst-20810	101	48	in	in	ADP
ajst-20810	101	49	the	the	DET
ajst-20810	101	50	next	next	ADJ
ajst-20810	101	51	layer	layer	NOUN
ajst-20810	101	52	,	,	PUNCT
ajst-20810	101	53	and	and	CCONJ
ajst-20810	101	54	then	then	ADV
ajst-20810	101	55	the	the	DET
ajst-20810	101	56	information	information	NOUN
ajst-20810	101	57	of	of	ADP
ajst-20810	101	58	the	the	DET
ajst-20810	101	59	output	output	NOUN
ajst-20810	101	60	channels	channel	NOUN
ajst-20810	101	61	and	and	CCONJ
ajst-20810	101	62	the	the	DET
ajst-20810	101	63	input	input	NOUN
ajst-20810	101	64	channels	channel	NOUN
ajst-20810	101	65	of	of	ADP
ajst-20810	101	66	different	different	ADJ
ajst-20810	101	67	groups	group	NOUN
ajst-20810	101	68	will	will	AUX
ajst-20810	101	69	interact	interact	VERB
ajst-20810	101	70	.	.	PUNCT
ajst-20810	102	1	in	in	ADP
ajst-20810	102	2	order	order	NOUN
ajst-20810	102	3	to	to	PART
ajst-20810	102	4	achieve	achieve	VERB
ajst-20810	102	5	the	the	DET
ajst-20810	102	6	goal	goal	NOUN
ajst-20810	102	7	,	,	PUNCT
ajst-20810	102	8	the	the	DET
ajst-20810	102	9	model	model	NOUN
ajst-20810	102	10	can	can	AUX
ajst-20810	102	11	be	be	AUX
ajst-20810	102	12	constructed	construct	VERB
ajst-20810	102	13	by	by	ADP
ajst-20810	102	14	channel	channel	NOUN
ajst-20810	102	15	disambiguation	disambiguation	PROPN
ajst-20810	102	16	operation	operation	NOUN
ajst-20810	102	17	to	to	PART
ajst-20810	102	18	realise	realise	VERB
ajst-20810	102	19	the	the	DET
ajst-20810	102	20	vision	vision	NOUN
ajst-20810	102	21	with	with	ADP
ajst-20810	102	22	as	as	ADV
ajst-20810	102	23	little	little	ADJ
ajst-20810	102	24	computational	computational	ADJ
ajst-20810	102	25	time	time	NOUN
ajst-20810	102	26	as	as	ADP
ajst-20810	102	27	possible	possible	ADJ
ajst-20810	102	28	(	(	PUNCT
ajst-20810	102	29	fig	fig	NOUN
ajst-20810	102	30	.	.	PUNCT
ajst-20810	103	1	3(c	3(c	NUM
ajst-20810	103	2	)	)	PUNCT
ajst-20810	103	3	):	):	PUNCT
ajst-20810	103	4	assuming	assume	VERB
ajst-20810	103	5	that	that	SCONJ
ajst-20810	103	6	the	the	DET
ajst-20810	103	7	input	input	NOUN
ajst-20810	103	8	of	of	ADP
ajst-20810	103	9	a	a	DET
ajst-20810	103	10	convolutional	convolutional	ADJ
ajst-20810	103	11	layer	layer	NOUN
ajst-20810	103	12	is	be	AUX
ajst-20810	103	13	divided	divide	VERB
ajst-20810	103	14	into	into	ADP
ajst-20810	103	15	a	a	DET
ajst-20810	103	16	different	different	ADJ
ajst-20810	103	17	group	group	NOUN
ajst-20810	103	18	of	of	ADP
ajst-20810	103	19	a	a	PRON
ajst-20810	103	20	,	,	PUNCT
ajst-20810	103	21	and	and	CCONJ
ajst-20810	103	22	its	its	PRON
ajst-20810	103	23	corresponding	corresponding	ADJ
ajst-20810	103	24	output	output	NOUN
ajst-20810	103	25	channels	channel	NOUN
ajst-20810	103	26	are	be	AUX
ajst-20810	103	27	a	a	DET
ajst-20810	103	28	b	b	NOUN
ajst-20810	103	29	´	´	NOUN
ajst-20810	103	30	;	;	PUNCT
ajst-20810	103	31	firstly	firstly	ADV
ajst-20810	103	32	,	,	PUNCT
ajst-20810	103	33	the	the	DET
ajst-20810	103	34	dimension	dimension	NOUN
ajst-20810	103	35	of	of	ADP
ajst-20810	103	36	the	the	DET
ajst-20810	103	37	output	output	NOUN
ajst-20810	103	38	channels	channel	NOUN
ajst-20810	103	39	is	be	AUX
ajst-20810	103	40	changed	change	VERB
ajst-20810	103	41	to	to	ADP
ajst-20810	103	42	(	(	PUNCT
ajst-20810	103	43	a	a	DET
ajst-20810	103	44	,	,	PUNCT
ajst-20810	103	45	b	b	NOUN
ajst-20810	103	46	)	)	PUNCT
ajst-20810	103	47	transposed	transpose	VERB
ajst-20810	103	48	to	to	ADP
ajst-20810	103	49	(	(	PUNCT
ajst-20810	103	50	b	b	NOUN
ajst-20810	103	51	,	,	PUNCT
ajst-20810	103	52	a	a	PRON
ajst-20810	103	53	)	)	PUNCT
ajst-20810	103	54	before	before	ADP
ajst-20810	103	55	being	be	AUX
ajst-20810	103	56	flattened	flatten	VERB
ajst-20810	103	57	and	and	CCONJ
ajst-20810	103	58	used	use	VERB
ajst-20810	103	59	as	as	ADP
ajst-20810	103	60	inputs	input	NOUN
ajst-20810	103	61	to	to	ADP
ajst-20810	103	62	the	the	DET
ajst-20810	103	63	next	next	ADJ
ajst-20810	103	64	layer	layer	NOUN
ajst-20810	103	65	.	.	PUNCT
ajst-20810	104	1	note	note	VERB
ajst-20810	104	2	that	that	SCONJ
ajst-20810	104	3	even	even	ADV
ajst-20810	104	4	if	if	SCONJ
ajst-20810	104	5	the	the	DET
ajst-20810	104	6	number	number	NOUN
ajst-20810	104	7	of	of	ADP
ajst-20810	104	8	channels	channel	NOUN
ajst-20810	104	9	in	in	ADP
ajst-20810	104	10	the	the	DET
ajst-20810	104	11	two	two	NUM
ajst-20810	104	12	groups	group	NOUN
ajst-20810	104	13	is	be	AUX
ajst-20810	104	14	not	not	PART
ajst-20810	104	15	equal	equal	ADJ
ajst-20810	104	16	,	,	PUNCT
ajst-20810	104	17	this	this	DET
ajst-20810	104	18	implementation	implementation	NOUN
ajst-20810	104	19	is	be	AUX
ajst-20810	104	20	not	not	PART
ajst-20810	104	21	affected	affect	VERB
ajst-20810	104	22	in	in	ADP
ajst-20810	104	23	any	any	DET
ajst-20810	104	24	way	way	NOUN
ajst-20810	104	25	and	and	CCONJ
ajst-20810	104	26	still	still	ADV
ajst-20810	104	27	achieves	achieve	VERB
ajst-20810	104	28	its	its	PRON
ajst-20810	104	29	desired	desire	VERB
ajst-20810	104	30	effect	effect	NOUN
ajst-20810	104	31	.	.	PUNCT
ajst-20810	105	1	in	in	ADP
ajst-20810	105	2	addition	addition	NOUN
ajst-20810	105	3	,	,	PUNCT
ajst-20810	105	4	the	the	DET
ajst-20810	105	5	channel	channel	NOUN
ajst-20810	105	6	disruption	disruption	NOUN
ajst-20810	105	7	is	be	AUX
ajst-20810	105	8	also	also	ADV
ajst-20810	105	9	differentiable	differentiable	ADJ
ajst-20810	105	10	,	,	PUNCT
ajst-20810	105	11	which	which	PRON
ajst-20810	105	12	means	mean	VERB
ajst-20810	105	13	that	that	SCONJ
ajst-20810	105	14	this	this	DET
ajst-20810	105	15	disruption	disruption	NOUN
ajst-20810	105	16	operation	operation	NOUN
ajst-20810	105	17	can	can	AUX
ajst-20810	105	18	be	be	AUX
ajst-20810	105	19	embedded	embed	VERB
ajst-20810	105	20	in	in	ADP
ajst-20810	105	21	the	the	DET
ajst-20810	105	22	network	network	NOUN
ajst-20810	105	23	structure	structure	NOUN
ajst-20810	105	24	to	to	PART
ajst-20810	105	25	participate	participate	VERB
ajst-20810	105	26	in	in	ADP
ajst-20810	105	27	end	end	NOUN
ajst-20810	105	28	-	-	PUNCT
ajst-20810	105	29	toend	toend	NOUN
ajst-20810	105	30	learning	learning	NOUN
ajst-20810	105	31	,	,	PUNCT
ajst-20810	105	32	reducing	reduce	VERB
ajst-20810	105	33	the	the	DET
ajst-20810	105	34	cost	cost	NOUN
ajst-20810	105	35	of	of	ADP
ajst-20810	105	36	redundant	redundant	ADJ
ajst-20810	105	37	operations	operation	NOUN
ajst-20810	105	38	.	.	PUNCT
ajst-20810	106	1	with	with	ADP
ajst-20810	106	2	the	the	DET
ajst-20810	106	3	proposed	propose	VERB
ajst-20810	106	4	method	method	NOUN
ajst-20810	106	5	of	of	ADP
ajst-20810	106	6	channel	channel	NOUN
ajst-20810	106	7	disruption	disruption	NOUN
ajst-20810	106	8	,	,	PUNCT
ajst-20810	106	9	building	build	VERB
ajst-20810	106	10	more	more	ADV
ajst-20810	106	11	robust	robust	ADJ
ajst-20810	106	12	structures	structure	NOUN
ajst-20810	106	13	using	use	VERB
ajst-20810	106	14	multiple	multiple	ADJ
ajst-20810	106	15	groups	group	NOUN
ajst-20810	106	16	of	of	ADP
ajst-20810	106	17	convolutional	convolutional	ADJ
ajst-20810	106	18	layers	layer	NOUN
ajst-20810	106	19	has	have	AUX
ajst-20810	106	20	thus	thus	ADV
ajst-20810	106	21	become	become	VERB
ajst-20810	106	22	possible	possible	ADJ
ajst-20810	106	23	.	.	PUNCT
ajst-20810	107	1	4.2	4.2	NUM
ajst-20810	107	2	.	.	PUNCT
ajst-20810	107	3	shufflenet	shufflenet	NOUN
ajst-20810	107	4	module	module	NOUN
ajst-20810	107	5	using	use	VERB
ajst-20810	107	6	the	the	DET
ajst-20810	107	7	channel	channel	NOUN
ajst-20810	107	8	disruption	disruption	NOUN
ajst-20810	107	9	approach	approach	NOUN
ajst-20810	107	10	,	,	PUNCT
ajst-20810	107	11	a	a	DET
ajst-20810	107	12	novel	novel	ADJ
ajst-20810	107	13	shumenet	shumenet	NOUN
ajst-20810	107	14	network	network	NOUN
ajst-20810	107	15	cell	cell	NOUN
ajst-20810	107	16	has	have	AUX
ajst-20810	107	17	been	be	AUX
ajst-20810	107	18	proposed	propose	VERB
ajst-20810	107	19	for	for	ADP
ajst-20810	107	20	designing	design	VERB
ajst-20810	107	21	the	the	DET
ajst-20810	107	22	structure	structure	NOUN
ajst-20810	107	23	of	of	ADP
ajst-20810	107	24	the	the	DET
ajst-20810	107	25	microminiature	microminiature	PROPN
ajst-20810	107	26	network	network	NOUN
ajst-20810	107	27	model	model	PROPN
ajst-20810	107	28	.	.	PUNCT
ajst-20810	108	1	the	the	DET
ajst-20810	108	2	structural	structural	ADJ
ajst-20810	108	3	details	detail	NOUN
ajst-20810	108	4	of	of	ADP
ajst-20810	108	5	the	the	DET
ajst-20810	108	6	baseline	baseline	NOUN
ajst-20810	108	7	model	model	NOUN
ajst-20810	108	8	can	can	AUX
ajst-20810	108	9	be	be	AUX
ajst-20810	108	10	seen	see	VERB
ajst-20810	108	11	in	in	ADP
ajst-20810	108	12	fig	fig	NOUN
ajst-20810	108	13	.	.	PUNCT
ajst-20810	109	1	4(a	4(a	NUM
ajst-20810	109	2	)	)	PUNCT
ajst-20810	109	3	.	.	PUNCT
ajst-20810	110	1	shumenet	shumenet	NOUN
ajst-20810	110	2	is	be	AUX
ajst-20810	110	3	different	different	ADJ
ajst-20810	110	4	in	in	ADP
ajst-20810	110	5	that	that	PRON
ajst-20810	110	6	,	,	PUNCT
ajst-20810	110	7	in	in	ADP
ajst-20810	110	8	its	its	PRON
ajst-20810	110	9	residual	residual	ADJ
ajst-20810	110	10	branch	branch	NOUN
ajst-20810	110	11	,	,	PUNCT
ajst-20810	110	12	for	for	ADP
ajst-20810	110	13	the	the	DET
ajst-20810	110	14	3	3	NUM
ajst-20810	110	15	3	3	NUM
ajst-20810	110	16	´	´	NOUN
ajst-20810	110	17	convolutional	convolutional	ADJ
ajst-20810	110	18	layers	layer	NOUN
ajst-20810	110	19	,	,	PUNCT
ajst-20810	110	20	the	the	DET
ajst-20810	110	21	model	model	NOUN
ajst-20810	110	22	applies	apply	VERB
ajst-20810	110	23	a	a	DET
ajst-20810	110	24	depth	depth	NOUN
ajst-20810	110	25	-	-	PUNCT
ajst-20810	110	26	separable	separable	NOUN
ajst-20810	110	27	convolution	convolution	NOUN
ajst-20810	110	28	on	on	ADP
ajst-20810	110	29	the	the	DET
ajst-20810	110	30	feature	feature	NOUN
ajst-20810	110	31	map	map	NOUN
ajst-20810	110	32	to	to	PART
ajst-20810	110	33	extract	extract	VERB
ajst-20810	110	34	spatial	spatial	ADJ
ajst-20810	110	35	information	information	NOUN
ajst-20810	110	36	.	.	PUNCT
ajst-20810	111	1	then	then	ADV
ajst-20810	111	2	,	,	PUNCT
ajst-20810	111	3	the	the	DET
ajst-20810	111	4	group	group	NOUN
ajst-20810	111	5	convolution	convolution	NOUN
ajst-20810	111	6	layer	layer	NOUN
ajst-20810	111	7	of	of	ADP
ajst-20810	111	8	1	1	NUM
ajst-20810	111	9	1	1	NUM
ajst-20810	111	10	´	´	NOUN
ajst-20810	111	11	is	be	AUX
ajst-20810	111	12	used	use	VERB
ajst-20810	111	13	to	to	PART
ajst-20810	111	14	replace	replace	VERB
ajst-20810	111	15	the	the	DET
ajst-20810	111	16	1	1	NUM
ajst-20810	111	17	1	1	NUM
ajst-20810	111	18	´	´	NOUN
ajst-20810	111	19	convolution	convolution	NOUN
ajst-20810	111	20	to	to	PART
ajst-20810	111	21	reduce	reduce	VERB
ajst-20810	111	22	the	the	DET
ajst-20810	111	23	model	model	NOUN
ajst-20810	111	24	computation	computation	NOUN
ajst-20810	111	25	,	,	PUNCT
ajst-20810	111	26	and	and	CCONJ
ajst-20810	111	27	then	then	ADV
ajst-20810	111	28	the	the	DET
ajst-20810	111	29	output	output	NOUN
ajst-20810	111	30	feature	feature	NOUN
ajst-20810	111	31	map	map	NOUN
ajst-20810	111	32	is	be	AUX
ajst-20810	111	33	channel	channel	NOUN
ajst-20810	111	34	disrupted	disrupt	VERB
ajst-20810	111	35	to	to	PART
ajst-20810	111	36	achieve	achieve	VERB
ajst-20810	111	37	the	the	DET
ajst-20810	111	38	purpose	purpose	NOUN
ajst-20810	111	39	of	of	ADP
ajst-20810	111	40	channel	channel	NOUN
ajst-20810	111	41	interaction	interaction	NOUN
ajst-20810	111	42	,	,	PUNCT
ajst-20810	111	43	so	so	ADV
ajst-20810	111	44	far	far	ADV
ajst-20810	111	45	a	a	DET
ajst-20810	111	46	shumlenet	shumlenet	NOUN
ajst-20810	111	47	unit	unit	NOUN
ajst-20810	111	48	is	be	AUX
ajst-20810	111	49	formed	form	VERB
ajst-20810	111	50	,	,	PUNCT
ajst-20810	111	51	as	as	SCONJ
ajst-20810	111	52	shown	show	VERB
ajst-20810	111	53	in	in	ADP
ajst-20810	111	54	fig	fig	NOUN
ajst-20810	111	55	.	.	PUNCT
ajst-20810	112	1	4(b	4(b	NUM
ajst-20810	112	2	)	)	PUNCT
ajst-20810	112	3	the	the	DET
ajst-20810	112	4	purpose	purpose	NOUN
ajst-20810	112	5	of	of	ADP
ajst-20810	112	6	the	the	DET
ajst-20810	112	7	second	second	ADJ
ajst-20810	112	8	1	1	NUM
ajst-20810	112	9	1	1	NUM
ajst-20810	112	10	´	´	NOUN
ajst-20810	112	11	convolution	convolution	NOUN
ajst-20810	112	12	is	be	AUX
ajst-20810	112	13	to	to	PART
ajst-20810	112	14	recover	recover	VERB
ajst-20810	112	15	the	the	DET
ajst-20810	112	16	channel	channel	NOUN
ajst-20810	112	17	dimensions	dimension	NOUN
ajst-20810	112	18	to	to	PART
ajst-20810	112	19	match	match	VERB
ajst-20810	112	20	the	the	DET
ajst-20810	112	21	residual	residual	ADJ
ajst-20810	112	22	-	-	PUNCT
ajst-20810	112	23	connected	connect	VERB
ajst-20810	112	24	data	datum	NOUN
ajst-20810	112	25	input	input	NOUN
ajst-20810	112	26	dimensions	dimension	NOUN
ajst-20810	112	27	.	.	PUNCT
ajst-20810	113	1	for	for	ADP
ajst-20810	113	2	simplicity	simplicity	NOUN
ajst-20810	113	3	,	,	PUNCT
ajst-20810	113	4	no	no	DET
ajst-20810	113	5	additional	additional	ADJ
ajst-20810	113	6	channel	channel	NOUN
ajst-20810	113	7	disruption	disruption	NOUN
ajst-20810	113	8	operation	operation	NOUN
ajst-20810	113	9	is	be	AUX
ajst-20810	113	10	applied	apply	VERB
ajst-20810	113	11	after	after	ADP
ajst-20810	113	12	the	the	DET
ajst-20810	113	13	second	second	ADJ
ajst-20810	113	14	1	1	NUM
ajst-20810	113	15	1	1	NUM
ajst-20810	113	16	´	´	NOUN
ajst-20810	113	17	layer	layer	NOUN
ajst-20810	113	18	,	,	PUNCT
ajst-20810	113	19	as	as	SCONJ
ajst-20810	113	20	the	the	DET
ajst-20810	113	21	channel	channel	NOUN
ajst-20810	113	22	exchange	exchange	NOUN
ajst-20810	113	23	in	in	ADP
ajst-20810	113	24	each	each	DET
ajst-20810	113	25	residual	residual	ADJ
ajst-20810	113	26	cell	cell	NOUN
ajst-20810	113	27	is	be	AUX
ajst-20810	113	28	sufficient	sufficient	ADJ
ajst-20810	113	29	and	and	CCONJ
ajst-20810	113	30	no	no	DET
ajst-20810	113	31	additional	additional	ADJ
ajst-20810	113	32	operation	operation	NOUN
ajst-20810	113	33	needs	need	VERB
ajst-20810	113	34	to	to	PART
ajst-20810	113	35	be	be	AUX
ajst-20810	113	36	introduced	introduce	VERB
ajst-20810	113	37	.	.	PUNCT
ajst-20810	114	1	the	the	DET
ajst-20810	114	2	use	use	NOUN
ajst-20810	114	3	of	of	ADP
ajst-20810	114	4	batch	batch	NOUN
ajst-20810	114	5	normalisation	normalisation	NOUN
ajst-20810	114	6	(	(	PUNCT
ajst-20810	114	7	bn	bn	NOUN
ajst-20810	114	8	)	)	PUNCT
ajst-20810	114	9	and	and	CCONJ
ajst-20810	114	10	nonlinearity	nonlinearity	NOUN
ajst-20810	114	11	similar	similar	ADJ
ajst-20810	114	12	to	to	ADP
ajst-20810	114	13	the	the	DET
ajst-20810	114	14	xception	xception	NOUN
ajst-20810	114	15	and	and	CCONJ
ajst-20810	114	16	resnext	resnext	NOUN
ajst-20810	114	17	models	model	NOUN
ajst-20810	114	18	,	,	PUNCT
ajst-20810	114	19	shufenet	shufenet	PROPN
ajst-20810	114	20	does	do	AUX
ajst-20810	114	21	not	not	PART
ajst-20810	114	22	use	use	VERB
ajst-20810	114	23	the	the	DET
ajst-20810	114	24	relu	relu	NOUN
ajst-20810	114	25	activation	activation	NOUN
ajst-20810	114	26	function	function	NOUN
ajst-20810	114	27	after	after	ADP
ajst-20810	114	28	deep	deep	ADJ
ajst-20810	114	29	convolution	convolution	NOUN
ajst-20810	114	30	as	as	ADP
ajst-20810	114	31	in	in	ADP
ajst-20810	114	32	the	the	DET
ajst-20810	114	33	case	case	NOUN
ajst-20810	114	34	of	of	ADP
ajst-20810	114	35	residual	residual	ADJ
ajst-20810	114	36	networks	network	NOUN
ajst-20810	114	37	.	.	PUNCT
ajst-20810	115	1	for	for	ADP
ajst-20810	115	2	the	the	DET
ajst-20810	115	3	shumenet	shumenet	ADJ
ajst-20810	115	4	application	application	NOUN
ajst-20810	115	5	,	,	PUNCT
ajst-20810	115	6	only	only	ADV
ajst-20810	115	7	two	two	NUM
ajst-20810	115	8	modifications	modification	NOUN
ajst-20810	115	9	were	be	AUX
ajst-20810	115	10	made	make	VERB
ajst-20810	115	11	(	(	PUNCT
ajst-20810	115	12	see	see	VERB
ajst-20810	115	13	fig	fig	NOUN
ajst-20810	115	14	.	.	PUNCT
ajst-20810	116	1	4(c	4(c	NUM
ajst-20810	116	2	)	)	PUNCT
ajst-20810	116	3	):	):	PUNCT
ajst-20810	117	1	(	(	PUNCT
ajst-20810	117	2	i	i	NOUN
ajst-20810	117	3	)	)	PUNCT
ajst-20810	117	4	a	a	DET
ajst-20810	117	5	window	window	NOUN
ajst-20810	117	6	of	of	ADP
ajst-20810	117	7	3	3	NUM
ajst-20810	117	8	average	average	ADJ
ajst-20810	117	9	pooling	pooling	NOUN
ajst-20810	117	10	was	be	AUX
ajst-20810	117	11	added	add	VERB
ajst-20810	117	12	to	to	ADP
ajst-20810	117	13	the	the	DET
ajst-20810	117	14	residual	residual	ADJ
ajst-20810	117	15	connections	connection	NOUN
ajst-20810	117	16	;	;	PUNCT
ajst-20810	117	17	and	and	CCONJ
ajst-20810	117	18	(	(	PUNCT
ajst-20810	117	19	ii	ii	NOUN
ajst-20810	117	20	)	)	PUNCT
ajst-20810	117	21	the	the	DET
ajst-20810	117	22	element	element	NOUN
ajst-20810	117	23	summing	sum	VERB
ajst-20810	117	24	was	be	AUX
ajst-20810	117	25	replaced	replace	VERB
ajst-20810	117	26	with	with	ADP
ajst-20810	117	27	channel	channel	NOUN
ajst-20810	117	28	splicing	splicing	NOUN
ajst-20810	117	29	,	,	PUNCT
ajst-20810	117	30	making	make	VERB
ajst-20810	117	31	the	the	DET
ajst-20810	117	32	channel	channel	NOUN
ajst-20810	117	33	dimension	dimension	NOUN
ajst-20810	117	34	easy	easy	ADJ
ajst-20810	117	35	to	to	PART
ajst-20810	117	36	scale	scale	VERB
ajst-20810	117	37	up	up	ADP
ajst-20810	117	38	without	without	ADP
ajst-20810	117	39	adding	add	VERB
ajst-20810	117	40	extra	extra	ADJ
ajst-20810	117	41	cost	cost	NOUN
ajst-20810	117	42	.	.	PUNCT
ajst-20810	118	1	figure	figure	NOUN
ajst-20810	118	2	4	4	NUM
ajst-20810	118	3	.	.	PUNCT
ajst-20810	119	1	shufenet	shufenet	PROPN
ajst-20810	119	2	network	network	NOUN
ajst-20810	119	3	units	unit	NOUN
ajst-20810	119	4	(	(	PUNCT
ajst-20810	119	5	a	a	DET
ajst-20810	119	6	)	)	PUNCT
ajst-20810	119	7	bottleneck	bottleneck	NOUN
ajst-20810	119	8	unit	unit	NOUN
ajst-20810	119	9	with	with	ADP
ajst-20810	119	10	deeply	deeply	ADV
ajst-20810	119	11	separable	separable	ADJ
ajst-20810	119	12	convolution	convolution	NOUN
ajst-20810	119	13	;	;	PUNCT
ajst-20810	119	14	(	(	PUNCT
ajst-20810	119	15	b	b	X
ajst-20810	119	16	)	)	PUNCT
ajst-20810	119	17	shufenet	shufenet	NOUN
ajst-20810	119	18	unit	unit	NOUN
ajst-20810	119	19	with	with	ADP
ajst-20810	119	20	1	1	NUM
ajst-20810	119	21	1	1	NUM
ajst-20810	119	22	´	´	NOUN
ajst-20810	119	23	group	group	NOUN
ajst-20810	119	24	convolution	convolution	NOUN
ajst-20810	119	25	and	and	CCONJ
ajst-20810	119	26	channel	channel	NOUN
ajst-20810	119	27	disambiguation	disambiguation	NOUN
ajst-20810	119	28	;	;	PUNCT
ajst-20810	119	29	(	(	PUNCT
ajst-20810	119	30	c	c	X
ajst-20810	119	31	)	)	PUNCT
ajst-20810	119	32	shufenet	shufenet	NOUN
ajst-20810	119	33	with	with	ADP
ajst-20810	119	34	step	step	NOUN
ajst-20810	119	35	size	size	NOUN
ajst-20810	119	36	2	2	NUM
ajst-20810	119	37	the	the	DET
ajst-20810	119	38	1	1	NUM
ajst-20810	119	39	1	1	NUM
ajst-20810	119	40	´	´	NOUN
ajst-20810	119	41	group	group	NOUN
ajst-20810	119	42	convolution	convolution	NOUN
ajst-20810	119	43	employs	employ	VERB
ajst-20810	119	44	channel	channel	NOUN
ajst-20810	119	45	disruption	disruption	NOUN
ajst-20810	119	46	,	,	PUNCT
ajst-20810	119	47	allowing	allow	VERB
ajst-20810	119	48	the	the	DET
ajst-20810	119	49	modular	modular	ADJ
ajst-20810	119	50	components	component	NOUN
ajst-20810	119	51	of	of	ADP
ajst-20810	119	52	the	the	DET
ajst-20810	119	53	shufenet	shufenet	NOUN
ajst-20810	119	54	model	model	NOUN
ajst-20810	119	55	to	to	PART
ajst-20810	119	56	be	be	AUX
ajst-20810	119	57	combined	combine	VERB
ajst-20810	119	58	efficiently	efficiently	ADV
ajst-20810	119	59	.	.	PUNCT
ajst-20810	120	1	compared	compare	VERB
ajst-20810	120	2	to	to	ADP
ajst-20810	120	3	resnet	resnet	NOUN
ajst-20810	120	4	and	and	CCONJ
ajst-20810	120	5	resnext	resnext	NOUN
ajst-20810	120	6	,	,	PUNCT
ajst-20810	120	7	the	the	DET
ajst-20810	120	8	structure	structure	NOUN
ajst-20810	120	9	of	of	ADP
ajst-20810	120	10	shumenet	shumenet	NOUN
ajst-20810	120	11	has	have	VERB
ajst-20810	120	12	lower	low	ADJ
ajst-20810	120	13	complexity	complexity	NOUN
ajst-20810	120	14	for	for	ADP
ajst-20810	120	15	the	the	DET
ajst-20810	120	16	same	same	ADJ
ajst-20810	120	17	setup	setup	NOUN
ajst-20810	120	18	.	.	PUNCT
ajst-20810	121	1	for	for	ADP
ajst-20810	121	2	example	example	NOUN
ajst-20810	121	3	,	,	PUNCT
ajst-20810	121	4	shufhenet	shufhenet	NOUN
ajst-20810	121	5	requires	require	VERB
ajst-20810	121	6	fewer	few	ADJ
ajst-20810	121	7	floating	float	VERB
ajst-20810	121	8	point	point	NOUN
ajst-20810	121	9	operations	operation	NOUN
ajst-20810	121	10	(	(	PUNCT
ajst-20810	121	11	fops	fop	NOUN
ajst-20810	121	12	)	)	PUNCT
ajst-20810	121	13	compared	compare	VERB
ajst-20810	121	14	to	to	ADP
ajst-20810	121	15	resnet	resnet	NOUN
ajst-20810	121	16	and	and	CCONJ
ajst-20810	121	17	resnext	resnext	NOUN
ajst-20810	121	18	for	for	ADP
ajst-20810	121	19	the	the	DET
ajst-20810	121	20	same	same	ADJ
ajst-20810	121	21	setup	setup	NOUN
ajst-20810	121	22	of	of	ADP
ajst-20810	121	23	input	input	NOUN
ajst-20810	121	24	size	size	NOUN
ajst-20810	121	25	and	and	CCONJ
ajst-20810	121	26	number	number	NOUN
ajst-20810	121	27	of	of	ADP
ajst-20810	121	28	bottleneck	bottleneck	NOUN
ajst-20810	121	29	channels	channel	NOUN
ajst-20810	121	30	.	.	PUNCT
ajst-20810	122	1	alternatively	alternatively	ADV
ajst-20810	122	2	,	,	PUNCT
ajst-20810	122	3	shumlenet	shumlenet	NOUN
ajst-20810	122	4	is	be	AUX
ajst-20810	122	5	able	able	ADJ
ajst-20810	122	6	to	to	PART
ajst-20810	122	7	use	use	VERB
ajst-20810	122	8	feature	feature	NOUN
ajst-20810	122	9	maps	map	NOUN
ajst-20810	122	10	with	with	ADP
ajst-20810	122	11	a	a	DET
ajst-20810	122	12	larger	large	ADJ
ajst-20810	122	13	number	number	NOUN
ajst-20810	122	14	of	of	ADP
ajst-20810	122	15	channels	channel	NOUN
ajst-20810	122	16	in	in	ADP
ajst-20810	122	17	the	the	DET
ajst-20810	122	18	same	same	ADJ
ajst-20810	122	19	computational	computational	ADJ
ajst-20810	122	20	budget	budget	NOUN
ajst-20810	122	21	.	.	PUNCT
ajst-20810	123	1	since	since	SCONJ
ajst-20810	123	2	smaller	small	ADJ
ajst-20810	123	3	networks	network	NOUN
ajst-20810	123	4	generally	generally	ADV
ajst-20810	123	5	do	do	AUX
ajst-20810	123	6	not	not	PART
ajst-20810	123	7	have	have	VERB
ajst-20810	123	8	more	more	ADJ
ajst-20810	123	9	channels	channel	NOUN
ajst-20810	123	10	to	to	PART
ajst-20810	123	11	process	process	VERB
ajst-20810	123	12	information	information	NOUN
ajst-20810	123	13	,	,	PUNCT
ajst-20810	123	14	the	the	DET
ajst-20810	123	15	advantage	advantage	NOUN
ajst-20810	123	16	of	of	ADP
ajst-20810	123	17	shufenet	shufenet	NOUN
ajst-20810	123	18	is	be	AUX
ajst-20810	123	19	quite	quite	ADV
ajst-20810	123	20	important	important	ADJ
ajst-20810	123	21	for	for	ADP
ajst-20810	123	22	them	they	PRON
ajst-20810	123	23	.	.	PUNCT
ajst-20810	124	1	in	in	ADP
ajst-20810	124	2	shumenet	shumenet	NOUN
ajst-20810	124	3	,	,	PUNCT
ajst-20810	124	4	only	only	ADV
ajst-20810	124	5	the	the	DET
ajst-20810	124	6	bottleneck	bottleneck	NOUN
ajst-20810	124	7	feature	feature	NOUN
ajst-20810	124	8	maps	map	NOUN
ajst-20810	124	9	are	be	AUX
ajst-20810	124	10	deeply	deeply	ADV
ajst-20810	124	11	convolved	convolve	VERB
ajst-20810	124	12	.	.	PUNCT
ajst-20810	125	1	this	this	PRON
ajst-20810	125	2	is	be	AUX
ajst-20810	125	3	mainly	mainly	ADV
ajst-20810	125	4	due	due	ADJ
ajst-20810	125	5	to	to	ADP
ajst-20810	125	6	the	the	DET
ajst-20810	125	7	fact	fact	NOUN
ajst-20810	125	8	that	that	SCONJ
ajst-20810	125	9	small	small	ADJ
ajst-20810	125	10	mobile	mobile	ADJ
ajst-20810	125	11	devices	device	NOUN
ajst-20810	125	12	have	have	AUX
ajst-20810	125	13	poor	poor	ADJ
ajst-20810	125	14	memory	memory	NOUN
ajst-20810	125	15	access	access	NOUN
ajst-20810	125	16	rates	rate	NOUN
ajst-20810	125	17	compared	compare	VERB
ajst-20810	125	18	to	to	ADP
ajst-20810	125	19	other	other	ADJ
ajst-20810	125	20	intensive	intensive	ADJ
ajst-20810	125	21	computing	compute	VERB
ajst-20810	125	22	operations	operation	NOUN
ajst-20810	125	23	due	due	ADP
ajst-20810	125	24	to	to	ADP
ajst-20810	125	25	their	their	PRON
ajst-20810	125	26	own	own	ADJ
ajst-20810	125	27	device	device	NOUN
ajst-20810	125	28	parameter	parameter	NOUN
ajst-20810	125	29	limitations	limitation	NOUN
ajst-20810	125	30	,	,	PUNCT
ajst-20810	125	31	so	so	CCONJ
ajst-20810	125	32	deep	deep	ADJ
ajst-20810	125	33	convolution	convolution	NOUN
ajst-20810	125	34	complexity	complexity	NOUN
ajst-20810	125	35	is	be	AUX
ajst-20810	125	36	difficult	difficult	ADJ
ajst-20810	125	37	to	to	PART
ajst-20810	125	38	accomplish	accomplish	VERB
ajst-20810	125	39	effectively	effectively	ADV
ajst-20810	125	40	on	on	ADP
ajst-20810	125	41	mobile	mobile	ADJ
ajst-20810	125	42	devices	device	NOUN
ajst-20810	125	43	despite	despite	SCONJ
ajst-20810	125	44	182	182	NUM
ajst-20810	125	45	the	the	DET
ajst-20810	125	46	fact	fact	NOUN
ajst-20810	125	47	that	that	SCONJ
ajst-20810	125	48	it	it	PRON
ajst-20810	125	49	has	have	AUX
ajst-20810	125	50	been	be	AUX
ajst-20810	125	51	theoretically	theoretically	ADV
ajst-20810	125	52	reduced	reduce	VERB
ajst-20810	125	53	to	to	ADP
ajst-20810	125	54	a	a	DET
ajst-20810	125	55	very	very	ADV
ajst-20810	125	56	low	low	ADJ
ajst-20810	125	57	level	level	NOUN
ajst-20810	125	58	.	.	PUNCT
ajst-20810	126	1	this	this	DET
ajst-20810	126	2	shortcoming	shortcoming	NOUN
ajst-20810	126	3	is	be	AUX
ajst-20810	126	4	also	also	ADV
ajst-20810	126	5	present	present	ADJ
ajst-20810	126	6	in	in	ADP
ajst-20810	126	7	shufnenet	shufnenet	NOUN
ajst-20810	126	8	,	,	PUNCT
ajst-20810	126	9	which	which	PRON
ajst-20810	126	10	has	have	VERB
ajst-20810	126	11	a	a	DET
ajst-20810	126	12	tensorflow	tensorflow	NOUN
ajst-20810	126	13	-	-	PUNCT
ajst-20810	126	14	based	base	VERB
ajst-20810	126	15	runtime	runtime	NOUN
ajst-20810	126	16	library	library	NOUN
ajst-20810	126	17	.	.	PUNCT
ajst-20810	127	1	in	in	ADP
ajst-20810	127	2	the	the	DET
ajst-20810	127	3	shumenet	shumenet	ADJ
ajst-20810	127	4	unit	unit	NOUN
ajst-20810	127	5	,	,	PUNCT
ajst-20810	127	6	deep	deep	ADJ
ajst-20810	127	7	separable	separable	ADJ
ajst-20810	127	8	convolutional	convolutional	ADJ
ajst-20810	127	9	operations	operation	NOUN
ajst-20810	127	10	are	be	AUX
ajst-20810	127	11	performed	perform	VERB
ajst-20810	127	12	only	only	ADV
ajst-20810	127	13	on	on	ADP
ajst-20810	127	14	the	the	DET
ajst-20810	127	15	bottleneck	bottleneck	NOUN
ajst-20810	127	16	layer	layer	NOUN
ajst-20810	127	17	,	,	PUNCT
ajst-20810	127	18	which	which	PRON
ajst-20810	127	19	minimises	minimise	NOUN
ajst-20810	127	20	redundant	redundant	VERB
ajst-20810	127	21	model	model	NOUN
ajst-20810	127	22	computation	computation	NOUN
ajst-20810	127	23	overhead	overhead	ADV
ajst-20810	127	24	.	.	PUNCT
ajst-20810	128	1	4.3	4.3	NUM
ajst-20810	128	2	.	.	PUNCT
ajst-20810	128	3	overall	overall	ADJ
ajst-20810	128	4	structure	structure	NOUN
ajst-20810	128	5	of	of	ADP
ajst-20810	128	6	the	the	DET
ajst-20810	128	7	shufflenet	shufflenet	NOUN
ajst-20810	128	8	network	network	NOUN
ajst-20810	128	9	shufienet	shufienet	NOUN
ajst-20810	128	10	network	network	NOUN
ajst-20810	128	11	,	,	PUNCT
ajst-20810	128	12	in	in	ADP
ajst-20810	128	13	terms	term	NOUN
ajst-20810	128	14	of	of	ADP
ajst-20810	128	15	model	model	NOUN
ajst-20810	128	16	structure	structure	NOUN
ajst-20810	128	17	design	design	NOUN
ajst-20810	128	18	,	,	PUNCT
ajst-20810	128	19	was	be	AUX
ajst-20810	128	20	chosen	choose	VERB
ajst-20810	128	21	to	to	PART
ajst-20810	128	22	design	design	VERB
ajst-20810	128	23	a	a	DET
ajst-20810	128	24	network	network	NOUN
ajst-20810	128	25	structure	structure	NOUN
ajst-20810	128	26	with	with	ADP
ajst-20810	128	27	better	well	ADJ
ajst-20810	128	28	performance	performance	NOUN
ajst-20810	128	29	in	in	ADP
ajst-20810	128	30	order	order	NOUN
ajst-20810	128	31	to	to	PART
ajst-20810	128	32	make	make	VERB
ajst-20810	128	33	the	the	DET
ajst-20810	128	34	model	model	NOUN
ajst-20810	128	35	faster	fast	ADV
ajst-20810	128	36	and	and	CCONJ
ajst-20810	128	37	model	model	NOUN
ajst-20810	128	38	size	size	NOUN
ajst-20810	128	39	reduction	reduction	NOUN
ajst-20810	128	40	.	.	PUNCT
ajst-20810	129	1	the	the	DET
ajst-20810	129	2	details	detail	NOUN
ajst-20810	129	3	of	of	ADP
ajst-20810	129	4	the	the	DET
ajst-20810	129	5	shumlenet	shumlenet	NOUN
ajst-20810	129	6	model	model	NOUN
ajst-20810	129	7	structure	structure	NOUN
ajst-20810	129	8	are	be	AUX
ajst-20810	129	9	illustrated	illustrate	VERB
ajst-20810	129	10	in	in	ADP
ajst-20810	129	11	fig	fig	NOUN
ajst-20810	129	12	.	.	PUNCT
ajst-20810	130	1	5	5	NUM
ajst-20810	130	2	the	the	DET
ajst-20810	130	3	network	network	NOUN
ajst-20810	130	4	is	be	AUX
ajst-20810	130	5	mainly	mainly	ADV
ajst-20810	130	6	composed	compose	VERB
ajst-20810	130	7	of	of	ADP
ajst-20810	130	8	a	a	DET
ajst-20810	130	9	stack	stack	NOUN
ajst-20810	130	10	of	of	ADP
ajst-20810	130	11	shumlenet	shumlenet	NOUN
ajst-20810	130	12	network	network	NOUN
ajst-20810	130	13	units	unit	NOUN
ajst-20810	130	14	which	which	PRON
ajst-20810	130	15	are	be	AUX
ajst-20810	130	16	mainly	mainly	ADV
ajst-20810	130	17	composed	compose	VERB
ajst-20810	130	18	of	of	ADP
ajst-20810	130	19	three	three	NUM
ajst-20810	130	20	stages	stage	NOUN
ajst-20810	130	21	.	.	PUNCT
ajst-20810	131	1	the	the	DET
ajst-20810	131	2	first	first	ADJ
ajst-20810	131	3	convolution	convolution	NOUN
ajst-20810	131	4	in	in	ADP
ajst-20810	131	5	each	each	DET
ajst-20810	131	6	stage	stage	NOUN
ajst-20810	131	7	is	be	AUX
ajst-20810	131	8	chosen	choose	VERB
ajst-20810	131	9	with	with	ADP
ajst-20810	131	10	a	a	DET
ajst-20810	131	11	step	step	NOUN
ajst-20810	131	12	size	size	NOUN
ajst-20810	131	13	of	of	ADP
ajst-20810	131	14	2	2	NUM
ajst-20810	131	15	,	,	PUNCT
ajst-20810	131	16	and	and	CCONJ
ajst-20810	131	17	the	the	DET
ajst-20810	131	18	subsequent	subsequent	ADJ
ajst-20810	131	19	steps	step	NOUN
ajst-20810	131	20	are	be	AUX
ajst-20810	131	21	changed	change	VERB
ajst-20810	131	22	according	accord	VERB
ajst-20810	131	23	to	to	ADP
ajst-20810	131	24	the	the	DET
ajst-20810	131	25	training	training	NOUN
ajst-20810	131	26	process	process	NOUN
ajst-20810	131	27	.	.	PUNCT
ajst-20810	132	1	the	the	DET
ajst-20810	132	2	other	other	ADJ
ajst-20810	132	3	parameters	parameter	NOUN
ajst-20810	132	4	in	in	ADP
ajst-20810	132	5	that	that	DET
ajst-20810	132	6	stage	stage	NOUN
ajst-20810	132	7	are	be	AUX
ajst-20810	132	8	not	not	PART
ajst-20810	132	9	adjusted	adjust	VERB
ajst-20810	132	10	,	,	PUNCT
ajst-20810	132	11	and	and	CCONJ
ajst-20810	132	12	for	for	ADP
ajst-20810	132	13	the	the	DET
ajst-20810	132	14	next	next	ADJ
ajst-20810	132	15	stage	stage	NOUN
ajst-20810	132	16	,	,	PUNCT
ajst-20810	132	17	the	the	DET
ajst-20810	132	18	number	number	NOUN
ajst-20810	132	19	of	of	ADP
ajst-20810	132	20	channels	channel	NOUN
ajst-20810	132	21	of	of	ADP
ajst-20810	132	22	the	the	DET
ajst-20810	132	23	output	output	NOUN
ajst-20810	132	24	data	datum	NOUN
ajst-20810	132	25	is	be	AUX
ajst-20810	132	26	doubled	double	VERB
ajst-20810	132	27	compared	compare	VERB
ajst-20810	132	28	to	to	ADP
ajst-20810	132	29	that	that	PRON
ajst-20810	132	30	of	of	ADP
ajst-20810	132	31	the	the	DET
ajst-20810	132	32	input	input	NOUN
ajst-20810	132	33	data	datum	NOUN
ajst-20810	132	34	,	,	PUNCT
ajst-20810	132	35	which	which	PRON
ajst-20810	132	36	is	be	AUX
ajst-20810	132	37	caused	cause	VERB
ajst-20810	132	38	by	by	ADP
ajst-20810	132	39	the	the	DET
ajst-20810	132	40	final	final	ADJ
ajst-20810	132	41	channel	channel	NOUN
ajst-20810	132	42	splicing	splicing	NOUN
ajst-20810	132	43	operation	operation	NOUN
ajst-20810	132	44	of	of	ADP
ajst-20810	132	45	the	the	DET
ajst-20810	132	46	shumenet	shumenet	ADJ
ajst-20810	132	47	network	network	NOUN
ajst-20810	132	48	cells	cell	NOUN
ajst-20810	132	49	.	.	PUNCT
ajst-20810	133	1	the	the	DET
ajst-20810	133	2	number	number	NOUN
ajst-20810	133	3	of	of	ADP
ajst-20810	133	4	bottleneck	bottleneck	NOUN
ajst-20810	133	5	channels	channel	NOUN
ajst-20810	133	6	for	for	ADP
ajst-20810	133	7	each	each	DET
ajst-20810	133	8	shumenet	shumenet	ADJ
ajst-20810	133	9	network	network	NOUN
ajst-20810	133	10	is	be	AUX
ajst-20810	133	11	set	set	VERB
ajst-20810	133	12	to	to	ADP
ajst-20810	133	13	1/4	1/4	NUM
ajst-20810	133	14	of	of	ADP
ajst-20810	133	15	the	the	DET
ajst-20810	133	16	output	output	NOUN
ajst-20810	133	17	channels	channel	NOUN
ajst-20810	133	18	,	,	PUNCT
ajst-20810	133	19	which	which	PRON
ajst-20810	133	20	is	be	AUX
ajst-20810	133	21	intended	intend	VERB
ajst-20810	133	22	to	to	PART
ajst-20810	133	23	reduce	reduce	VERB
ajst-20810	133	24	the	the	DET
ajst-20810	133	25	network	network	NOUN
ajst-20810	133	26	parameters	parameter	NOUN
ajst-20810	133	27	and	and	CCONJ
ajst-20810	133	28	make	make	VERB
ajst-20810	133	29	the	the	DET
ajst-20810	133	30	model	model	NOUN
ajst-20810	133	31	more	more	ADV
ajst-20810	133	32	simplified	simplified	ADJ
ajst-20810	133	33	,	,	PUNCT
ajst-20810	133	34	but	but	CCONJ
ajst-20810	133	35	it	it	PRON
ajst-20810	133	36	is	be	AUX
ajst-20810	133	37	mentioned	mention	VERB
ajst-20810	133	38	in	in	ADP
ajst-20810	133	39	shufflenet	shufflenet	NOUN
ajst-20810	133	40	that	that	SCONJ
ajst-20810	133	41	more	more	ADJ
ajst-20810	133	42	hyper	hyper	ADJ
ajst-20810	133	43	-	-	ADJ
ajst-20810	133	44	parameter	parameter	ADJ
ajst-20810	133	45	tuning	tuning	NOUN
ajst-20810	133	46	may	may	AUX
ajst-20810	133	47	give	give	VERB
ajst-20810	133	48	better	well	ADJ
ajst-20810	133	49	results	result	NOUN
ajst-20810	133	50	.	.	PUNCT
ajst-20810	134	1	in	in	ADP
ajst-20810	134	2	the	the	DET
ajst-20810	134	3	shufienet	shufienet	NOUN
ajst-20810	134	4	unit	unit	NOUN
ajst-20810	134	5	,	,	PUNCT
ajst-20810	134	6	the	the	DET
ajst-20810	134	7	number	number	NOUN
ajst-20810	134	8	of	of	ADP
ajst-20810	134	9	groups	group	NOUN
ajst-20810	134	10	g	g	PROPN
ajst-20810	134	11	controls	control	VERB
ajst-20810	134	12	the	the	DET
ajst-20810	134	13	sparsity	sparsity	NOUN
ajst-20810	134	14	of	of	ADP
ajst-20810	134	15	the	the	DET
ajst-20810	134	16	convolution	convolution	NOUN
ajst-20810	134	17	's	's	PART
ajst-20810	134	18	connections	connection	NOUN
ajst-20810	134	19	over	over	ADP
ajst-20810	134	20	the	the	DET
ajst-20810	134	21	channels	channel	NOUN
ajst-20810	134	22	.	.	PUNCT
ajst-20810	135	1	figure	figure	NOUN
ajst-20810	135	2	5	5	NUM
ajst-20810	135	3	discusses	discuss	VERB
ajst-20810	135	4	various	various	ADJ
ajst-20810	135	5	numbers	number	NOUN
ajst-20810	135	6	of	of	ADP
ajst-20810	135	7	groups	group	NOUN
ajst-20810	135	8	(	(	PUNCT
ajst-20810	135	9	all	all	DET
ajst-20810	135	10	approximately	approximately	ADV
ajst-20810	135	11	140	140	NUM
ajst-20810	135	12	mflops	mflop	NOUN
ajst-20810	135	13	)	)	PUNCT
ajst-20810	135	14	that	that	PRON
ajst-20810	135	15	adjust	adjust	VERB
ajst-20810	135	16	the	the	DET
ajst-20810	135	17	output	output	NOUN
ajst-20810	135	18	channels	channel	NOUN
ajst-20810	135	19	to	to	PART
ajst-20810	135	20	ensure	ensure	VERB
ajst-20810	135	21	that	that	SCONJ
ajst-20810	135	22	the	the	DET
ajst-20810	135	23	overall	overall	ADJ
ajst-20810	135	24	computational	computational	ADJ
ajst-20810	135	25	complexity	complexity	NOUN
ajst-20810	135	26	is	be	AUX
ajst-20810	135	27	essentially	essentially	ADV
ajst-20810	135	28	the	the	DET
ajst-20810	135	29	same	same	ADJ
ajst-20810	135	30	.	.	PUNCT
ajst-20810	136	1	it	it	PRON
ajst-20810	136	2	is	be	AUX
ajst-20810	136	3	clear	clear	ADJ
ajst-20810	136	4	that	that	SCONJ
ajst-20810	136	5	,	,	PUNCT
ajst-20810	136	6	for	for	ADP
ajst-20810	136	7	a	a	DET
ajst-20810	136	8	given	give	VERB
ajst-20810	136	9	complexity	complexity	NOUN
ajst-20810	136	10	constraint	constraint	NOUN
ajst-20810	136	11	,	,	PUNCT
ajst-20810	136	12	a	a	DET
ajst-20810	136	13	larger	large	ADJ
ajst-20810	136	14	number	number	NOUN
ajst-20810	136	15	of	of	ADP
ajst-20810	136	16	groups	group	NOUN
ajst-20810	136	17	will	will	AUX
ajst-20810	136	18	increase	increase	VERB
ajst-20810	136	19	the	the	DET
ajst-20810	136	20	number	number	NOUN
ajst-20810	136	21	of	of	ADP
ajst-20810	136	22	output	output	NOUN
ajst-20810	136	23	channels	channel	NOUN
ajst-20810	136	24	,	,	PUNCT
ajst-20810	136	25	which	which	PRON
ajst-20810	136	26	will	will	AUX
ajst-20810	136	27	result	result	VERB
ajst-20810	136	28	in	in	ADP
ajst-20810	136	29	more	more	ADJ
ajst-20810	136	30	convolutional	convolutional	ADJ
ajst-20810	136	31	filters	filter	NOUN
ajst-20810	136	32	being	be	AUX
ajst-20810	136	33	generated	generate	VERB
ajst-20810	136	34	,	,	PUNCT
ajst-20810	136	35	thus	thus	ADV
ajst-20810	136	36	allowing	allow	VERB
ajst-20810	136	37	more	more	ADJ
ajst-20810	136	38	information	information	NOUN
ajst-20810	136	39	to	to	PART
ajst-20810	136	40	be	be	AUX
ajst-20810	136	41	encoded	encode	VERB
ajst-20810	136	42	,	,	PUNCT
ajst-20810	136	43	although	although	SCONJ
ajst-20810	136	44	this	this	PRON
ajst-20810	136	45	will	will	AUX
ajst-20810	136	46	also	also	ADV
ajst-20810	136	47	result	result	VERB
ajst-20810	136	48	in	in	ADP
ajst-20810	136	49	degradation	degradation	NOUN
ajst-20810	136	50	of	of	ADP
ajst-20810	136	51	a	a	DET
ajst-20810	136	52	single	single	ADJ
ajst-20810	136	53	convolutional	convolutional	ADJ
ajst-20810	136	54	filter	filter	NOUN
ajst-20810	136	55	due	due	ADP
ajst-20810	136	56	to	to	ADP
ajst-20810	136	57	its	its	PRON
ajst-20810	136	58	respective	respective	ADJ
ajst-20810	136	59	input	input	NOUN
ajst-20810	136	60	channel.shumenet	channel.shumenet	PROPN
ajst-20810	136	61	examines	examine	VERB
ajst-20810	136	62	the	the	DET
ajst-20810	136	63	effect	effect	NOUN
ajst-20810	136	64	of	of	ADP
ajst-20810	136	65	different	different	ADJ
ajst-20810	136	66	computational	computational	ADJ
ajst-20810	136	67	constraints	constraint	NOUN
ajst-20810	136	68	on	on	ADP
ajst-20810	136	69	this	this	DET
ajst-20810	136	70	number	number	NOUN
ajst-20810	136	71	of	of	ADP
ajst-20810	136	72	channels	channel	NOUN
ajst-20810	136	73	.	.	PUNCT
ajst-20810	137	1	customising	customise	VERB
ajst-20810	137	2	the	the	DET
ajst-20810	137	3	network	network	NOUN
ajst-20810	137	4	to	to	ADP
ajst-20810	137	5	the	the	DET
ajst-20810	137	6	desired	desire	VERB
ajst-20810	137	7	complexity	complexity	NOUN
ajst-20810	137	8	only	only	ADV
ajst-20810	137	9	requires	require	VERB
ajst-20810	137	10	applying	apply	VERB
ajst-20810	137	11	the	the	DET
ajst-20810	137	12	scale	scale	NOUN
ajst-20810	137	13	factor	factor	NOUN
ajst-20810	137	14	s	s	NOUN
ajst-20810	137	15	to	to	ADP
ajst-20810	137	16	the	the	DET
ajst-20810	137	17	number	number	NOUN
ajst-20810	137	18	of	of	ADP
ajst-20810	137	19	channels	channel	NOUN
ajst-20810	137	20	.	.	PUNCT
ajst-20810	138	1	figure	figure	VERB
ajst-20810	138	2	5	5	NUM
ajst-20810	138	3	.	.	PUNCT
ajst-20810	138	4	shuffle	shuffle	PROPN
ajst-20810	138	5	network	network	NOUN
ajst-20810	138	6	structure	structure	NOUN
ajst-20810	138	7	5	5	NUM
ajst-20810	138	8	.	.	PUNCT
ajst-20810	138	9	results	result	VERB
ajst-20810	138	10	5.1	5.1	NUM
ajst-20810	138	11	.	.	PUNCT
ajst-20810	139	1	image	image	NOUN
ajst-20810	139	2	classification	classification	NOUN
ajst-20810	139	3	based	base	VERB
ajst-20810	139	4	on	on	ADP
ajst-20810	139	5	resnet	resnet	NOUN
ajst-20810	139	6	model	model	NOUN
ajst-20810	139	7	with	with	ADP
ajst-20810	139	8	the	the	DET
ajst-20810	139	9	increase	increase	NOUN
ajst-20810	139	10	of	of	ADP
ajst-20810	139	11	the	the	DET
ajst-20810	139	12	number	number	NOUN
ajst-20810	139	13	of	of	ADP
ajst-20810	139	14	layers	layer	NOUN
ajst-20810	139	15	of	of	ADP
ajst-20810	139	16	the	the	DET
ajst-20810	139	17	deep	deep	ADJ
ajst-20810	139	18	network	network	NOUN
ajst-20810	139	19	,	,	PUNCT
ajst-20810	139	20	better	well	ADJ
ajst-20810	139	21	feature	feature	NOUN
ajst-20810	139	22	learning	learning	NOUN
ajst-20810	139	23	can	can	AUX
ajst-20810	139	24	be	be	AUX
ajst-20810	139	25	carried	carry	VERB
ajst-20810	139	26	out	out	ADP
ajst-20810	139	27	,	,	PUNCT
ajst-20810	139	28	but	but	CCONJ
ajst-20810	139	29	it	it	PRON
ajst-20810	139	30	also	also	ADV
ajst-20810	139	31	leads	lead	VERB
ajst-20810	139	32	to	to	ADP
ajst-20810	139	33	the	the	DET
ajst-20810	139	34	re	re	NOUN
ajst-20810	139	35	-	-	NOUN
ajst-20810	139	36	propagation	propagation	NOUN
ajst-20810	139	37	process	process	NOUN
ajst-20810	139	38	in	in	ADP
ajst-20810	139	39	the	the	DET
ajst-20810	139	40	network	network	NOUN
ajst-20810	139	41	can	can	AUX
ajst-20810	139	42	become	become	VERB
ajst-20810	139	43	unstable	unstable	ADJ
ajst-20810	139	44	.	.	PUNCT
ajst-20810	140	1	the	the	DET
ajst-20810	140	2	design	design	NOUN
ajst-20810	140	3	of	of	ADP
ajst-20810	140	4	resnet	resnet	NOUN
ajst-20810	140	5	model	model	NOUN
ajst-20810	140	6	makes	make	VERB
ajst-20810	140	7	the	the	DET
ajst-20810	140	8	internal	internal	ADJ
ajst-20810	140	9	structure	structure	NOUN
ajst-20810	140	10	of	of	ADP
ajst-20810	140	11	the	the	DET
ajst-20810	140	12	network	network	NOUN
ajst-20810	140	13	model	model	NOUN
ajst-20810	140	14	has	have	VERB
ajst-20810	140	15	the	the	DET
ajst-20810	140	16	ability	ability	NOUN
ajst-20810	140	17	of	of	ADP
ajst-20810	140	18	certain	certain	ADJ
ajst-20810	140	19	constant	constant	ADJ
ajst-20810	140	20	mapping	mapping	NOUN
ajst-20810	140	21	,	,	PUNCT
ajst-20810	140	22	and	and	CCONJ
ajst-20810	140	23	solves	solve	VERB
ajst-20810	140	24	the	the	DET
ajst-20810	140	25	problem	problem	NOUN
ajst-20810	140	26	of	of	ADP
ajst-20810	140	27	gradient	gradient	ADJ
ajst-20810	140	28	explosion	explosion	NOUN
ajst-20810	140	29	or	or	CCONJ
ajst-20810	140	30	gradient	gradient	ADJ
ajst-20810	140	31	disappearance	disappearance	NOUN
ajst-20810	140	32	by	by	ADP
ajst-20810	140	33	adding	add	VERB
ajst-20810	140	34	the	the	DET
ajst-20810	140	35	residual	residual	ADJ
ajst-20810	140	36	connection	connection	NOUN
ajst-20810	140	37	,	,	PUNCT
ajst-20810	140	38	which	which	PRON
ajst-20810	140	39	enhances	enhance	VERB
ajst-20810	140	40	the	the	DET
ajst-20810	140	41	stability	stability	NOUN
ajst-20810	140	42	in	in	ADP
ajst-20810	140	43	the	the	DET
ajst-20810	140	44	propagation	propagation	NOUN
ajst-20810	140	45	of	of	ADP
ajst-20810	140	46	the	the	DET
ajst-20810	140	47	network	network	NOUN
ajst-20810	140	48	,	,	PUNCT
ajst-20810	140	49	and	and	CCONJ
ajst-20810	140	50	effectively	effectively	ADV
ajst-20810	140	51	accelerates	accelerate	VERB
ajst-20810	140	52	the	the	DET
ajst-20810	140	53	convergence	convergence	NOUN
ajst-20810	140	54	of	of	ADP
ajst-20810	140	55	the	the	DET
ajst-20810	140	56	network	network	NOUN
ajst-20810	140	57	,	,	PUNCT
ajst-20810	140	58	and	and	CCONJ
ajst-20810	140	59	the	the	DET
ajst-20810	140	60	degradation	degradation	NOUN
ajst-20810	140	61	problem	problem	NOUN
ajst-20810	140	62	of	of	ADP
ajst-20810	140	63	the	the	DET
ajst-20810	140	64	deep	deep	ADJ
ajst-20810	140	65	network	network	NOUN
ajst-20810	140	66	can	can	AUX
ajst-20810	140	67	be	be	AUX
ajst-20810	140	68	solved	solve	VERB
ajst-20810	140	69	as	as	ADP
ajst-20810	140	70	a	a	DET
ajst-20810	140	71	result	result	NOUN
ajst-20810	140	72	.	.	PUNCT
ajst-20810	141	1	5.1.1	5.1.1	X
ajst-20810	141	2	.	.	PUNCT
ajst-20810	141	3	comparison	comparison	NOUN
ajst-20810	141	4	of	of	ADP
ajst-20810	141	5	classification	classification	NOUN
ajst-20810	141	6	performance	performance	NOUN
ajst-20810	141	7	based	base	VERB
ajst-20810	141	8	on	on	ADP
ajst-20810	141	9	different	different	ADJ
ajst-20810	141	10	networks	network	NOUN
ajst-20810	141	11	the	the	DET
ajst-20810	141	12	classification	classification	NOUN
ajst-20810	141	13	performance	performance	NOUN
ajst-20810	141	14	of	of	ADP
ajst-20810	141	15	the	the	DET
ajst-20810	141	16	resnet	resnet	NOUN
ajst-20810	141	17	model	model	NOUN
ajst-20810	141	18	will	will	AUX
ajst-20810	141	19	be	be	AUX
ajst-20810	141	20	evaluated	evaluate	VERB
ajst-20810	141	21	on	on	ADP
ajst-20810	141	22	a	a	DET
ajst-20810	141	23	flower	flower	NOUN
ajst-20810	141	24	dataset	dataset	NOUN
ajst-20810	141	25	,	,	PUNCT
ajst-20810	141	26	as	as	SCONJ
ajst-20810	141	27	shown	show	VERB
ajst-20810	141	28	in	in	ADP
ajst-20810	141	29	fig	fig	NOUN
ajst-20810	141	30	.	.	PUNCT
ajst-20810	142	1	6	6	NUM
ajst-20810	142	2	for	for	ADP
ajst-20810	142	3	some	some	PRON
ajst-20810	142	4	of	of	ADP
ajst-20810	142	5	the	the	DET
ajst-20810	142	6	images	image	NOUN
ajst-20810	142	7	.	.	PUNCT
ajst-20810	143	1	the	the	DET
ajst-20810	143	2	selected	select	VERB
ajst-20810	143	3	flower	flower	NOUN
ajst-20810	143	4	dataset	dataset	NOUN
ajst-20810	143	5	is	be	AUX
ajst-20810	143	6	nearly	nearly	ADV
ajst-20810	143	7	4000	4000	NUM
ajst-20810	143	8	images	image	NOUN
ajst-20810	143	9	in	in	ADP
ajst-20810	143	10	total	total	NOUN
ajst-20810	143	11	,	,	PUNCT
ajst-20810	143	12	with	with	ADP
ajst-20810	143	13	a	a	DET
ajst-20810	143	14	total	total	ADJ
ajst-20810	143	15	size	size	NOUN
ajst-20810	143	16	of	of	ADP
ajst-20810	143	17	about	about	ADV
ajst-20810	143	18	200	200	NUM
ajst-20810	143	19	m	m	NOUN
ajst-20810	143	20	,	,	PUNCT
ajst-20810	143	21	which	which	PRON
ajst-20810	143	22	can	can	AUX
ajst-20810	143	23	be	be	AUX
ajst-20810	143	24	classified	classify	VERB
ajst-20810	143	25	into	into	ADP
ajst-20810	143	26	5	5	NUM
ajst-20810	143	27	different	different	ADJ
ajst-20810	143	28	types	type	NOUN
ajst-20810	143	29	of	of	ADP
ajst-20810	143	30	flower	flower	NOUN
ajst-20810	143	31	images	image	NOUN
ajst-20810	143	32	,	,	PUNCT
ajst-20810	143	33	namely	namely	ADV
ajst-20810	143	34	sunflower	sunflower	NOUN
ajst-20810	143	35	,	,	PUNCT
ajst-20810	143	36	tulip	tulip	VERB
ajst-20810	143	37	,	,	PUNCT
ajst-20810	143	38	rose	rise	VERB
ajst-20810	143	39	,	,	PUNCT
ajst-20810	143	40	dandelion	dandelion	NOUN
ajst-20810	143	41	,	,	PUNCT
ajst-20810	143	42	and	and	CCONJ
ajst-20810	143	43	daisy	daisy	NOUN
ajst-20810	143	44	,	,	PUNCT
ajst-20810	143	45	and	and	CCONJ
ajst-20810	143	46	each	each	DET
ajst-20810	143	47	type	type	NOUN
ajst-20810	143	48	contains	contain	VERB
ajst-20810	143	49	600~900	600~900	NUM
ajst-20810	143	50	images	image	NOUN
ajst-20810	143	51	.	.	PUNCT
ajst-20810	144	1	the	the	DET
ajst-20810	144	2	size	size	NOUN
ajst-20810	144	3	size	size	NOUN
ajst-20810	144	4	of	of	ADP
ajst-20810	144	5	the	the	DET
ajst-20810	144	6	pictures	picture	NOUN
ajst-20810	144	7	in	in	ADP
ajst-20810	144	8	the	the	DET
ajst-20810	144	9	training	training	NOUN
ajst-20810	144	10	set	set	VERB
ajst-20810	144	11	in	in	ADP
ajst-20810	144	12	the	the	DET
ajst-20810	144	13	dataset	dataset	NOUN
ajst-20810	144	14	is	be	AUX
ajst-20810	144	15	not	not	PART
ajst-20810	144	16	uniform	uniform	ADJ
ajst-20810	144	17	,	,	PUNCT
ajst-20810	144	18	the	the	DET
ajst-20810	144	19	common	common	ADJ
ajst-20810	144	20	size	size	NOUN
ajst-20810	144	21	is	be	AUX
ajst-20810	144	22	320×240	320×240	NUM
ajst-20810	144	23	or	or	CCONJ
ajst-20810	144	24	180×240	180×240	NUM
ajst-20810	144	25	,	,	PUNCT
ajst-20810	144	26	etc	etc	X
ajst-20810	144	27	.	.	X
ajst-20810	144	28	,	,	PUNCT
ajst-20810	144	29	and	and	CCONJ
ajst-20810	144	30	the	the	DET
ajst-20810	144	31	size	size	NOUN
ajst-20810	144	32	of	of	ADP
ajst-20810	144	33	the	the	DET
ajst-20810	144	34	pictures	picture	NOUN
ajst-20810	144	35	ranges	range	VERB
ajst-20810	144	36	from	from	ADP
ajst-20810	144	37	30	30	NUM
ajst-20810	144	38	to	to	PART
ajst-20810	144	39	150	150	NUM
ajst-20810	144	40	kb	kb	PROPN
ajst-20810	144	41	,	,	PUNCT
ajst-20810	144	42	and	and	CCONJ
ajst-20810	144	43	the	the	DET
ajst-20810	144	44	data	data	NOUN
ajst-20810	144	45	is	be	AUX
ajst-20810	144	46	not	not	PART
ajst-20810	144	47	pure	pure	ADJ
ajst-20810	144	48	,	,	PUNCT
ajst-20810	144	49	which	which	PRON
ajst-20810	144	50	is	be	AUX
ajst-20810	144	51	mixed	mix	VERB
ajst-20810	144	52	with	with	ADP
ajst-20810	144	53	some	some	DET
ajst-20810	144	54	other	other	ADJ
ajst-20810	144	55	pictures	picture	NOUN
ajst-20810	144	56	.	.	PUNCT
ajst-20810	145	1	for	for	ADP
ajst-20810	145	2	subsequent	subsequent	ADJ
ajst-20810	145	3	experiments	experiment	NOUN
ajst-20810	145	4	,	,	PUNCT
ajst-20810	145	5	the	the	DET
ajst-20810	145	6	flower	flower	NOUN
ajst-20810	145	7	dataset	dataset	NOUN
ajst-20810	145	8	is	be	AUX
ajst-20810	145	9	divided	divide	VERB
ajst-20810	145	10	into	into	ADP
ajst-20810	145	11	two	two	NUM
ajst-20810	145	12	parts	part	NOUN
ajst-20810	145	13	:	:	PUNCT
ajst-20810	145	14	flower_train	flower_train	VERB
ajst-20810	145	15	and	and	CCONJ
ajst-20810	145	16	flower_test	flower_t	ADJ
ajst-20810	145	17	.	.	PUNCT
ajst-20810	146	1	figure	figure	NOUN
ajst-20810	146	2	6	6	NUM
ajst-20810	146	3	.	.	PUNCT
ajst-20810	146	4	selected	select	VERB
ajst-20810	146	5	data	data	NOUN
ajst-20810	146	6	sets	set	VERB
ajst-20810	146	7	we	we	PRON
ajst-20810	146	8	validate	validate	VERB
ajst-20810	146	9	the	the	DET
ajst-20810	146	10	effect	effect	NOUN
ajst-20810	146	11	of	of	ADP
ajst-20810	146	12	the	the	DET
ajst-20810	146	13	number	number	NOUN
ajst-20810	146	14	of	of	ADP
ajst-20810	146	15	resnet	resnet	ADJ
ajst-20810	146	16	layers	layer	NOUN
ajst-20810	146	17	on	on	ADP
ajst-20810	146	18	classification	classification	NOUN
ajst-20810	146	19	performance	performance	NOUN
ajst-20810	146	20	by	by	ADP
ajst-20810	146	21	choosing	choose	VERB
ajst-20810	146	22	resnet-18	resnet-18	PROPN
ajst-20810	146	23	,	,	PUNCT
ajst-20810	146	24	resnet34	resnet34	NOUN
ajst-20810	146	25	,	,	PUNCT
ajst-20810	146	26	resnet-50	resnet-50	PROPN
ajst-20810	146	27	,	,	PUNCT
ajst-20810	146	28	resnet101	resnet101	PROPN
ajst-20810	146	29	,	,	PUNCT
ajst-20810	146	30	resnet-152,the	resnet-152,the	PRON
ajst-20810	146	31	number	number	NOUN
ajst-20810	146	32	of	of	ADP
ajst-20810	146	33	training	training	NOUN
ajst-20810	146	34	validation	validation	NOUN
ajst-20810	146	35	rounds	round	NOUN
ajst-20810	146	36	is	be	AUX
ajst-20810	146	37	20	20	NUM
ajst-20810	146	38	,	,	PUNCT
ajst-20810	146	39	and	and	CCONJ
ajst-20810	146	40	the	the	DET
ajst-20810	146	41	classification	classification	NOUN
ajst-20810	146	42	accuracy	accuracy	NOUN
ajst-20810	146	43	metrics	metric	NOUN
ajst-20810	146	44	are	be	AUX
ajst-20810	146	45	denoted	denote	VERB
ajst-20810	146	46	by	by	ADP
ajst-20810	146	47	the	the	DET
ajst-20810	146	48	total	total	ADJ
ajst-20810	146	49	accuracy	accuracy	NOUN
ajst-20810	146	50	oa	oa	INTJ
ajst-20810	146	51	.	.	NOUN
ajst-20810	146	52	183	183	NUM
ajst-20810	146	53	table	table	NOUN
ajst-20810	146	54	1	1	NUM
ajst-20810	146	55	.	.	PUNCT
ajst-20810	147	1	comparison	comparison	NOUN
ajst-20810	147	2	of	of	ADP
ajst-20810	147	3	classification	classification	NOUN
ajst-20810	147	4	performance	performance	NOUN
ajst-20810	147	5	with	with	ADP
ajst-20810	147	6	different	different	ADJ
ajst-20810	147	7	network	network	NOUN
ajst-20810	147	8	depths	depth	VERB
ajst-20810	147	9	layers	layer	NOUN
ajst-20810	147	10	acc%	acc%	PROPN
ajst-20810	147	11	flops/109	flops/109	PROPN
ajst-20810	147	12	resnet-18	resnet-18	PROPN
ajst-20810	147	13	93.22	93.22	NUM
ajst-20810	147	14	1.8	1.8	NUM
ajst-20810	147	15	resnet-34	resnet-34	PROPN
ajst-20810	147	16	93.42	93.42	NUM
ajst-20810	147	17	3.6	3.6	NUM
ajst-20810	147	18	resnet-50	resnet-50	NOUN
ajst-20810	147	19	94.01	94.01	NUM
ajst-20810	147	20	3.8	3.8	NUM
ajst-20810	147	21	resnet-101	resnet-101	VERB
ajst-20810	147	22	94.55	94.55	NUM
ajst-20810	147	23	7.6	7.6	NUM
ajst-20810	147	24	resnet-152	resnet-152	NOUN
ajst-20810	147	25	94.63	94.63	NUM
ajst-20810	147	26	11.3	11.3	NUM
ajst-20810	147	27	from	from	ADP
ajst-20810	147	28	table	table	NOUN
ajst-20810	147	29	1	1	NUM
ajst-20810	147	30	,	,	PUNCT
ajst-20810	147	31	it	it	PRON
ajst-20810	147	32	can	can	AUX
ajst-20810	147	33	be	be	AUX
ajst-20810	147	34	seen	see	VERB
ajst-20810	147	35	that	that	SCONJ
ajst-20810	147	36	as	as	ADP
ajst-20810	147	37	the	the	DET
ajst-20810	147	38	number	number	NOUN
ajst-20810	147	39	of	of	ADP
ajst-20810	147	40	convolutional	convolutional	ADJ
ajst-20810	147	41	layers	layer	NOUN
ajst-20810	147	42	increases	increase	VERB
ajst-20810	147	43	,	,	PUNCT
ajst-20810	147	44	the	the	DET
ajst-20810	147	45	overall	overall	ADJ
ajst-20810	147	46	accuracy	accuracy	NOUN
ajst-20810	147	47	also	also	ADV
ajst-20810	147	48	increases	increase	VERB
ajst-20810	147	49	,	,	PUNCT
ajst-20810	147	50	and	and	CCONJ
ajst-20810	147	51	it	it	PRON
ajst-20810	147	52	can	can	AUX
ajst-20810	147	53	be	be	AUX
ajst-20810	147	54	concluded	conclude	VERB
ajst-20810	147	55	that	that	SCONJ
ajst-20810	147	56	the	the	DET
ajst-20810	147	57	accuracy	accuracy	NOUN
ajst-20810	147	58	of	of	ADP
ajst-20810	147	59	resnet	resnet	NOUN
ajst-20810	147	60	based	base	VERB
ajst-20810	147	61	image	image	NOUN
ajst-20810	147	62	classification	classification	NOUN
ajst-20810	147	63	increases	increase	NOUN
ajst-20810	147	64	as	as	ADP
ajst-20810	147	65	the	the	DET
ajst-20810	147	66	number	number	NOUN
ajst-20810	147	67	of	of	ADP
ajst-20810	147	68	convolutional	convolutional	ADJ
ajst-20810	147	69	layers	layer	NOUN
ajst-20810	147	70	increases	increase	NOUN
ajst-20810	147	71	.	.	PUNCT
ajst-20810	148	1	this	this	PRON
ajst-20810	148	2	is	be	AUX
ajst-20810	148	3	due	due	ADJ
ajst-20810	148	4	to	to	ADP
ajst-20810	148	5	the	the	DET
ajst-20810	148	6	fact	fact	NOUN
ajst-20810	148	7	that	that	SCONJ
ajst-20810	148	8	as	as	SCONJ
ajst-20810	148	9	the	the	DET
ajst-20810	148	10	number	number	NOUN
ajst-20810	148	11	of	of	ADP
ajst-20810	148	12	layers	layer	NOUN
ajst-20810	148	13	increases	increase	VERB
ajst-20810	148	14	,	,	PUNCT
ajst-20810	148	15	the	the	DET
ajst-20810	148	16	depth	depth	NOUN
ajst-20810	148	17	of	of	ADP
ajst-20810	148	18	the	the	DET
ajst-20810	148	19	network	network	NOUN
ajst-20810	148	20	and	and	CCONJ
ajst-20810	148	21	the	the	DET
ajst-20810	148	22	performance	performance	NOUN
ajst-20810	148	23	ability	ability	NOUN
ajst-20810	148	24	of	of	ADP
ajst-20810	148	25	the	the	DET
ajst-20810	148	26	model	model	NOUN
ajst-20810	148	27	gets	get	VERB
ajst-20810	148	28	better	well	ADJ
ajst-20810	148	29	and	and	CCONJ
ajst-20810	148	30	the	the	DET
ajst-20810	148	31	overall	overall	ADJ
ajst-20810	148	32	accuracy	accuracy	NOUN
ajst-20810	148	33	is	be	AUX
ajst-20810	148	34	higher	high	ADJ
ajst-20810	148	35	.	.	PUNCT
ajst-20810	149	1	also	also	ADV
ajst-20810	149	2	,	,	PUNCT
ajst-20810	149	3	the	the	DET
ajst-20810	149	4	floating	float	VERB
ajst-20810	149	5	point	point	NOUN
ajst-20810	149	6	computing	compute	VERB
ajst-20810	149	7	data	datum	NOUN
ajst-20810	149	8	in	in	ADP
ajst-20810	149	9	the	the	DET
ajst-20810	149	10	table	table	NOUN
ajst-20810	149	11	shows	show	VERB
ajst-20810	149	12	an	an	DET
ajst-20810	149	13	increase	increase	NOUN
ajst-20810	149	14	in	in	ADP
ajst-20810	149	15	computing	compute	VERB
ajst-20810	149	16	complexity	complexity	NOUN
ajst-20810	149	17	with	with	ADP
ajst-20810	149	18	increasing	increase	VERB
ajst-20810	149	19	number	number	NOUN
ajst-20810	149	20	of	of	ADP
ajst-20810	149	21	convolutional	convolutional	ADJ
ajst-20810	149	22	layers	layer	NOUN
ajst-20810	149	23	without	without	ADP
ajst-20810	149	24	any	any	DET
ajst-20810	149	25	decrease	decrease	NOUN
ajst-20810	149	26	in	in	ADP
ajst-20810	149	27	accuracy	accuracy	NOUN
ajst-20810	149	28	.	.	PUNCT
ajst-20810	150	1	this	this	PRON
ajst-20810	150	2	is	be	AUX
ajst-20810	150	3	because	because	SCONJ
ajst-20810	150	4	as	as	SCONJ
ajst-20810	150	5	the	the	DET
ajst-20810	150	6	depth	depth	NOUN
ajst-20810	150	7	of	of	ADP
ajst-20810	150	8	the	the	DET
ajst-20810	150	9	network	network	NOUN
ajst-20810	150	10	gets	get	VERB
ajst-20810	150	11	deeper	deep	ADJ
ajst-20810	150	12	,	,	PUNCT
ajst-20810	150	13	the	the	DET
ajst-20810	150	14	number	number	NOUN
ajst-20810	150	15	of	of	ADP
ajst-20810	150	16	layers	layer	NOUN
ajst-20810	150	17	increases	increase	VERB
ajst-20810	150	18	and	and	CCONJ
ajst-20810	150	19	the	the	DET
ajst-20810	150	20	computational	computational	ADJ
ajst-20810	150	21	complexity	complexity	NOUN
ajst-20810	150	22	required	require	VERB
ajst-20810	150	23	increases	increase	NOUN
ajst-20810	150	24	.	.	PUNCT
ajst-20810	151	1	5.2	5.2	NUM
ajst-20810	151	2	.	.	PUNCT
ajst-20810	152	1	image	image	NOUN
ajst-20810	152	2	classification	classification	NOUN
ajst-20810	152	3	based	base	VERB
ajst-20810	152	4	on	on	ADP
ajst-20810	152	5	shufflenet	shufflenet	NOUN
ajst-20810	152	6	model	model	NOUN
ajst-20810	152	7	with	with	ADP
ajst-20810	152	8	the	the	DET
ajst-20810	152	9	development	development	NOUN
ajst-20810	152	10	of	of	ADP
ajst-20810	152	11	deep	deep	ADJ
ajst-20810	152	12	learning	learning	NOUN
ajst-20810	152	13	,	,	PUNCT
ajst-20810	152	14	the	the	DET
ajst-20810	152	15	network	network	NOUN
ajst-20810	152	16	structure	structure	NOUN
ajst-20810	152	17	of	of	ADP
ajst-20810	152	18	cnns	cnns	PROPN
ajst-20810	152	19	is	be	AUX
ajst-20810	152	20	getting	get	VERB
ajst-20810	152	21	deeper	deep	ADJ
ajst-20810	152	22	and	and	CCONJ
ajst-20810	152	23	deeper	deep	ADJ
ajst-20810	152	24	,	,	PUNCT
ajst-20810	152	25	and	and	CCONJ
ajst-20810	152	26	most	most	ADJ
ajst-20810	152	27	high	high	ADJ
ajst-20810	152	28	-	-	PUNCT
ajst-20810	152	29	precision	precision	NOUN
ajst-20810	152	30	neural	neural	ADJ
ajst-20810	152	31	networks	network	NOUN
ajst-20810	152	32	need	need	VERB
ajst-20810	152	33	to	to	PART
ajst-20810	152	34	undergo	undergo	VERB
ajst-20810	152	35	billions	billion	NOUN
ajst-20810	152	36	of	of	ADP
ajst-20810	152	37	computations	computation	NOUN
ajst-20810	152	38	,	,	PUNCT
ajst-20810	152	39	which	which	PRON
ajst-20810	152	40	makes	make	VERB
ajst-20810	152	41	most	most	ADJ
ajst-20810	152	42	network	network	NOUN
ajst-20810	152	43	models	model	NOUN
ajst-20810	152	44	difficult	difficult	ADJ
ajst-20810	152	45	to	to	PART
ajst-20810	152	46	use	use	VERB
ajst-20810	152	47	on	on	ADP
ajst-20810	152	48	devices	device	NOUN
ajst-20810	152	49	with	with	ADP
ajst-20810	152	50	low	low	ADJ
ajst-20810	152	51	computational	computational	ADJ
ajst-20810	152	52	power	power	NOUN
ajst-20810	152	53	.	.	PUNCT
ajst-20810	153	1	the	the	DET
ajst-20810	153	2	shufflenet	shufflenet	NOUN
ajst-20810	153	3	model	model	NOUN
ajst-20810	153	4	,	,	PUNCT
ajst-20810	153	5	on	on	ADP
ajst-20810	153	6	the	the	DET
ajst-20810	153	7	other	other	ADJ
ajst-20810	153	8	hand	hand	NOUN
ajst-20810	153	9	,	,	PUNCT
ajst-20810	153	10	pursues	pursue	VERB
ajst-20810	153	11	optimal	optimal	ADJ
ajst-20810	153	12	accuracy	accuracy	NOUN
ajst-20810	153	13	under	under	ADP
ajst-20810	153	14	a	a	DET
ajst-20810	153	15	relatively	relatively	ADV
ajst-20810	153	16	small	small	ADJ
ajst-20810	153	17	budget	budget	NOUN
ajst-20810	153	18	of	of	ADP
ajst-20810	153	19	computational	computational	ADJ
ajst-20810	153	20	resources	resource	NOUN
ajst-20810	153	21	by	by	ADP
ajst-20810	153	22	using	use	VERB
ajst-20810	153	23	channel	channel	NOUN
ajst-20810	153	24	disruption	disruption	NOUN
ajst-20810	153	25	,	,	PUNCT
ajst-20810	153	26	which	which	PRON
ajst-20810	153	27	allows	allow	VERB
ajst-20810	153	28	the	the	DET
ajst-20810	153	29	input	input	NOUN
ajst-20810	153	30	and	and	CCONJ
ajst-20810	153	31	output	output	NOUN
ajst-20810	153	32	channel	channel	PROPN
ajst-20810	153	33	information	information	NOUN
ajst-20810	153	34	flows	flow	VERB
ajst-20810	153	35	to	to	PART
ajst-20810	153	36	interact	interact	VERB
ajst-20810	153	37	.	.	PUNCT
ajst-20810	154	1	for	for	ADP
ajst-20810	154	2	the	the	DET
ajst-20810	154	3	same	same	ADJ
ajst-20810	154	4	computational	computational	ADJ
ajst-20810	154	5	complexity	complexity	NOUN
ajst-20810	154	6	budget	budget	NOUN
ajst-20810	154	7	,	,	PUNCT
ajst-20810	154	8	the	the	DET
ajst-20810	154	9	shufflenet	shufflenet	NOUN
ajst-20810	154	10	model	model	NOUN
ajst-20810	154	11	allows	allow	VERB
ajst-20810	154	12	more	more	ADJ
ajst-20810	154	13	channels	channel	NOUN
ajst-20810	154	14	to	to	PART
ajst-20810	154	15	be	be	AUX
ajst-20810	154	16	used	use	VERB
ajst-20810	154	17	than	than	ADP
ajst-20810	154	18	other	other	ADJ
ajst-20810	154	19	common	common	ADJ
ajst-20810	154	20	architectures	architecture	NOUN
ajst-20810	154	21	,	,	PUNCT
ajst-20810	154	22	helping	help	VERB
ajst-20810	154	23	to	to	PART
ajst-20810	154	24	encode	encode	VERB
ajst-20810	154	25	more	more	ADJ
ajst-20810	154	26	information	information	NOUN
ajst-20810	154	27	.	.	PUNCT
ajst-20810	155	1	especially	especially	ADV
ajst-20810	155	2	when	when	SCONJ
ajst-20810	155	3	used	use	VERB
ajst-20810	155	4	in	in	ADP
ajst-20810	155	5	small	small	ADJ
ajst-20810	155	6	networks	network	NOUN
ajst-20810	155	7	,	,	PUNCT
ajst-20810	155	8	it	it	PRON
ajst-20810	155	9	has	have	VERB
ajst-20810	155	10	better	well	ADJ
ajst-20810	155	11	performance	performance	NOUN
ajst-20810	155	12	,	,	PUNCT
ajst-20810	155	13	which	which	PRON
ajst-20810	155	14	makes	make	VERB
ajst-20810	155	15	it	it	PRON
ajst-20810	155	16	popular	popular	ADJ
ajst-20810	155	17	for	for	ADP
ajst-20810	155	18	small	small	ADJ
ajst-20810	155	19	devices	device	NOUN
ajst-20810	155	20	.	.	PUNCT
ajst-20810	156	1	5.2.1	5.2.1	X
ajst-20810	156	2	.	.	PUNCT
ajst-20810	156	3	effect	effect	NOUN
ajst-20810	156	4	of	of	ADP
ajst-20810	156	5	group	group	NOUN
ajst-20810	156	6	size	size	NOUN
ajst-20810	156	7	on	on	ADP
ajst-20810	156	8	classification	classification	NOUN
ajst-20810	156	9	performance	performance	NOUN
ajst-20810	156	10	the	the	DET
ajst-20810	156	11	classification	classification	NOUN
ajst-20810	156	12	performance	performance	NOUN
ajst-20810	156	13	of	of	ADP
ajst-20810	156	14	the	the	DET
ajst-20810	156	15	shufflenet	shufflenet	NOUN
ajst-20810	156	16	model	model	NOUN
ajst-20810	156	17	was	be	AUX
ajst-20810	156	18	also	also	ADV
ajst-20810	156	19	evaluated	evaluate	VERB
ajst-20810	156	20	on	on	ADP
ajst-20810	156	21	the	the	DET
ajst-20810	156	22	floral	floral	ADJ
ajst-20810	156	23	dataset	dataset	NOUN
ajst-20810	156	24	mentioned	mention	VERB
ajst-20810	156	25	above	above	ADV
ajst-20810	156	26	.	.	PUNCT
ajst-20810	157	1	in	in	ADP
ajst-20810	157	2	order	order	NOUN
ajst-20810	157	3	to	to	PART
ajst-20810	157	4	assess	assess	VERB
ajst-20810	157	5	the	the	DET
ajst-20810	157	6	importance	importance	NOUN
ajst-20810	157	7	of	of	ADP
ajst-20810	157	8	1×1	1×1	NUM
ajst-20810	157	9	group	group	NOUN
ajst-20810	157	10	convolution	convolution	NOUN
ajst-20810	157	11	for	for	ADP
ajst-20810	157	12	the	the	DET
ajst-20810	157	13	shufflenet	shufflenet	NOUN
ajst-20810	157	14	model	model	NOUN
ajst-20810	157	15	,	,	PUNCT
ajst-20810	157	16	we	we	PRON
ajst-20810	157	17	verified	verify	VERB
ajst-20810	157	18	the	the	DET
ajst-20810	157	19	effect	effect	NOUN
ajst-20810	157	20	of	of	ADP
ajst-20810	157	21	shufflenet	shufflenet	NOUN
ajst-20810	157	22	group	group	NOUN
ajst-20810	157	23	size	size	NOUN
ajst-20810	157	24	g	g	NOUN
ajst-20810	157	25	on	on	ADP
ajst-20810	157	26	the	the	DET
ajst-20810	157	27	classification	classification	NOUN
ajst-20810	157	28	performance	performance	NOUN
ajst-20810	157	29	when	when	SCONJ
ajst-20810	157	30	the	the	DET
ajst-20810	157	31	number	number	NOUN
ajst-20810	157	32	of	of	ADP
ajst-20810	157	33	experimental	experimental	ADJ
ajst-20810	157	34	training	training	NOUN
ajst-20810	157	35	rounds	round	NOUN
ajst-20810	157	36	was	be	AUX
ajst-20810	157	37	chosen	choose	VERB
ajst-20810	157	38	to	to	PART
ajst-20810	157	39	be	be	AUX
ajst-20810	157	40	30	30	NUM
ajst-20810	157	41	,	,	PUNCT
ajst-20810	157	42	and	and	CCONJ
ajst-20810	157	43	the	the	DET
ajst-20810	157	44	number	number	NOUN
ajst-20810	157	45	of	of	ADP
ajst-20810	157	46	groups	group	NOUN
ajst-20810	157	47	chosen	choose	VERB
ajst-20810	157	48	was	be	AUX
ajst-20810	157	49	g	g	PROPN
ajst-20810	157	50	=	=	PUNCT
ajst-20810	157	51	{	{	PUNCT
ajst-20810	157	52	1	1	NUM
ajst-20810	157	53	2	2	NUM
ajst-20810	157	54	3	3	NUM
ajst-20810	157	55	4	4	NUM
ajst-20810	157	56	8},the	8},the	DET
ajst-20810	157	57	classification	classification	NOUN
ajst-20810	157	58	accuracy	accuracy	NOUN
ajst-20810	157	59	metric	metric	NOUN
ajst-20810	157	60	was	be	AUX
ajst-20810	157	61	expressed	express	VERB
ajst-20810	157	62	as	as	ADP
ajst-20810	157	63	the	the	DET
ajst-20810	157	64	total	total	ADJ
ajst-20810	157	65	accuracy	accuracy	NOUN
ajst-20810	157	66	oa	oa	INTJ
ajst-20810	157	67	.	.	NOUN
ajst-20810	157	68	table	table	NOUN
ajst-20810	157	69	2	2	NUM
ajst-20810	157	70	.	.	PUNCT
ajst-20810	157	71	effect	effect	NOUN
ajst-20810	157	72	of	of	ADP
ajst-20810	157	73	different	different	ADJ
ajst-20810	157	74	group	group	NOUN
ajst-20810	157	75	sizes	size	NOUN
ajst-20810	157	76	on	on	ADP
ajst-20810	157	77	classification	classification	NOUN
ajst-20810	157	78	performance	performance	NOUN
ajst-20810	157	79	shufflenet	shufflenet	NOUN
ajst-20810	157	80	groups	group	NOUN
ajst-20810	157	81	acc%	acc%	ADP
ajst-20810	157	82	flops/109	flops/109	PROPN
ajst-20810	157	83	g=1	g=1	PROPN
ajst-20810	157	84	93.24	93.24	NUM
ajst-20810	157	85	0.143	0.143	NUM
ajst-20810	157	86	g=2	g=2	PRON
ajst-20810	157	87	94.31	94.31	NUM
ajst-20810	157	88	0.140	0.140	NUM
ajst-20810	157	89	g=3	g=3	NOUN
ajst-20810	157	90	94.33	94.33	NUM
ajst-20810	157	91	0.137	0.137	NUM
ajst-20810	157	92	g=4	g=4	PROPN
ajst-20810	157	93	94.43	94.43	NUM
ajst-20810	157	94	0.133	0.133	NUM
ajst-20810	157	95	g=8	g=8	NOUN
ajst-20810	157	96	94.35	94.35	NUM
ajst-20810	157	97	0.130	0.130	NUM
ajst-20810	157	98	from	from	ADP
ajst-20810	157	99	table	table	NOUN
ajst-20810	157	100	2	2	NUM
ajst-20810	157	101	,	,	PUNCT
ajst-20810	157	102	it	it	PRON
ajst-20810	157	103	can	can	AUX
ajst-20810	157	104	be	be	AUX
ajst-20810	157	105	seen	see	VERB
ajst-20810	157	106	that	that	SCONJ
ajst-20810	157	107	the	the	DET
ajst-20810	157	108	overall	overall	ADJ
ajst-20810	157	109	accuracy	accuracy	NOUN
ajst-20810	157	110	increases	increase	VERB
ajst-20810	157	111	with	with	ADP
ajst-20810	157	112	the	the	DET
ajst-20810	157	113	increase	increase	NOUN
ajst-20810	157	114	in	in	ADP
ajst-20810	157	115	the	the	DET
ajst-20810	157	116	number	number	NOUN
ajst-20810	157	117	of	of	ADP
ajst-20810	157	118	groups	group	NOUN
ajst-20810	157	119	g	g	PROPN
ajst-20810	157	120	,	,	PUNCT
ajst-20810	157	121	which	which	PRON
ajst-20810	157	122	indicates	indicate	VERB
ajst-20810	157	123	that	that	SCONJ
ajst-20810	157	124	the	the	DET
ajst-20810	157	125	accuracy	accuracy	NOUN
ajst-20810	157	126	increases	increase	VERB
ajst-20810	157	127	with	with	ADP
ajst-20810	157	128	the	the	DET
ajst-20810	157	129	increase	increase	NOUN
ajst-20810	157	130	in	in	ADP
ajst-20810	157	131	the	the	DET
ajst-20810	157	132	number	number	NOUN
ajst-20810	157	133	of	of	ADP
ajst-20810	157	134	groups	group	NOUN
ajst-20810	157	135	.	.	PUNCT
ajst-20810	158	1	at	at	ADP
ajst-20810	158	2	the	the	DET
ajst-20810	158	3	same	same	ADJ
ajst-20810	158	4	time	time	NOUN
ajst-20810	158	5	,	,	PUNCT
ajst-20810	158	6	the	the	DET
ajst-20810	158	7	floating	float	VERB
ajst-20810	158	8	point	point	NOUN
ajst-20810	158	9	operation	operation	NOUN
ajst-20810	158	10	data	datum	NOUN
ajst-20810	158	11	in	in	ADP
ajst-20810	158	12	the	the	DET
ajst-20810	158	13	table	table	NOUN
ajst-20810	158	14	also	also	ADV
ajst-20810	158	15	shows	show	VERB
ajst-20810	158	16	that	that	SCONJ
ajst-20810	158	17	the	the	DET
ajst-20810	158	18	operation	operation	NOUN
ajst-20810	158	19	complexity	complexity	NOUN
ajst-20810	158	20	decreases	decrease	VERB
ajst-20810	158	21	with	with	ADP
ajst-20810	158	22	the	the	DET
ajst-20810	158	23	increase	increase	NOUN
ajst-20810	158	24	in	in	ADP
ajst-20810	158	25	the	the	DET
ajst-20810	158	26	number	number	NOUN
ajst-20810	158	27	of	of	ADP
ajst-20810	158	28	groups	group	NOUN
ajst-20810	158	29	g	g	VERB
ajst-20810	158	30	without	without	ADP
ajst-20810	158	31	any	any	DET
ajst-20810	158	32	decrease	decrease	NOUN
ajst-20810	158	33	in	in	ADP
ajst-20810	158	34	accuracy	accuracy	NOUN
ajst-20810	158	35	.	.	PUNCT
ajst-20810	159	1	table	table	NOUN
ajst-20810	159	2	2	2	NUM
ajst-20810	159	3	also	also	ADV
ajst-20810	159	4	shows	show	VERB
ajst-20810	159	5	that	that	SCONJ
ajst-20810	159	6	when	when	SCONJ
ajst-20810	159	7	the	the	DET
ajst-20810	159	8	number	number	NOUN
ajst-20810	159	9	of	of	ADP
ajst-20810	159	10	groups	group	NOUN
ajst-20810	159	11	g	g	PROPN
ajst-20810	159	12	becomes	become	VERB
ajst-20810	159	13	relatively	relatively	ADV
ajst-20810	159	14	large	large	ADJ
ajst-20810	159	15	,	,	PUNCT
ajst-20810	159	16	the	the	DET
ajst-20810	159	17	overall	overall	ADJ
ajst-20810	159	18	accuracy	accuracy	NOUN
ajst-20810	159	19	of	of	ADP
ajst-20810	159	20	classification	classification	NOUN
ajst-20810	159	21	decreases	decrease	NOUN
ajst-20810	159	22	,	,	PUNCT
ajst-20810	159	23	but	but	CCONJ
ajst-20810	159	24	the	the	DET
ajst-20810	159	25	results	result	NOUN
ajst-20810	159	26	are	be	AUX
ajst-20810	159	27	still	still	ADV
ajst-20810	159	28	better	well	ADJ
ajst-20810	159	29	than	than	ADP
ajst-20810	159	30	without	without	ADP
ajst-20810	159	31	channel	channel	NOUN
ajst-20810	159	32	disruption	disruption	NOUN
ajst-20810	159	33	.	.	PUNCT
ajst-20810	160	1	5.2.2	5.2.2	NUM
ajst-20810	160	2	.	.	PUNCT
ajst-20810	160	3	ablation	ablation	NOUN
ajst-20810	160	4	experiments	experiment	NOUN
ajst-20810	160	5	with	with	ADP
ajst-20810	160	6	shufflenet	shufflenet	NOUN
ajst-20810	160	7	modules	module	NOUN
ajst-20810	160	8	in	in	ADP
ajst-20810	160	9	order	order	NOUN
ajst-20810	160	10	to	to	PART
ajst-20810	160	11	verify	verify	VERB
ajst-20810	160	12	the	the	DET
ajst-20810	160	13	importance	importance	NOUN
ajst-20810	160	14	of	of	ADP
ajst-20810	160	15	channel	channel	NOUN
ajst-20810	160	16	shuffle	shuffle	NOUN
ajst-20810	160	17	in	in	ADP
ajst-20810	160	18	the	the	DET
ajst-20810	160	19	shufflenet	shufflenet	NOUN
ajst-20810	160	20	model	model	NOUN
ajst-20810	160	21	,	,	PUNCT
ajst-20810	160	22	we	we	PRON
ajst-20810	160	23	conducted	conduct	VERB
ajst-20810	160	24	experiments	experiment	NOUN
ajst-20810	160	25	on	on	ADP
ajst-20810	160	26	it	it	PRON
ajst-20810	160	27	.	.	PUNCT
ajst-20810	161	1	we	we	PRON
ajst-20810	161	2	operate	operate	VERB
ajst-20810	161	3	on	on	ADP
ajst-20810	161	4	two	two	NUM
ajst-20810	161	5	different	different	ADJ
ajst-20810	161	6	groups	group	NOUN
ajst-20810	161	7	g	g	VERB
ajst-20810	161	8	with	with	ADP
ajst-20810	161	9	and	and	CCONJ
ajst-20810	161	10	without	without	ADP
ajst-20810	161	11	channel	channel	NOUN
ajst-20810	161	12	shuffle	shuffle	NOUN
ajst-20810	161	13	.	.	PUNCT
ajst-20810	162	1	the	the	DET
ajst-20810	162	2	classification	classification	NOUN
ajst-20810	162	3	accuracy	accuracy	NOUN
ajst-20810	162	4	metrics	metric	NOUN
ajst-20810	162	5	of	of	ADP
ajst-20810	162	6	the	the	DET
ajst-20810	162	7	experimental	experimental	ADJ
ajst-20810	162	8	results	result	NOUN
ajst-20810	162	9	are	be	AUX
ajst-20810	162	10	expressed	express	VERB
ajst-20810	162	11	as	as	ADP
ajst-20810	162	12	the	the	DET
ajst-20810	162	13	overall	overall	ADJ
ajst-20810	162	14	accuracy	accuracy	NOUN
ajst-20810	162	15	oa	oa	INTJ
ajst-20810	162	16	.	.	NOUN
ajst-20810	162	17	table	table	NOUN
ajst-20810	162	18	3	3	NUM
ajst-20810	162	19	.	.	PUNCT
ajst-20810	162	20	ablation	ablation	NOUN
ajst-20810	162	21	studies	study	NOUN
ajst-20810	162	22	on	on	ADP
ajst-20810	162	23	whether	whether	SCONJ
ajst-20810	162	24	channels	channel	NOUN
ajst-20810	162	25	are	be	AUX
ajst-20810	162	26	shuffle	shuffle	ADJ
ajst-20810	162	27	or	or	CCONJ
ajst-20810	162	28	not	not	PART
ajst-20810	162	29	with	with	ADP
ajst-20810	162	30	different	different	ADJ
ajst-20810	162	31	shufflenet	shufflenet	NOUN
ajst-20810	162	32	modules	module	NOUN
ajst-20810	162	33	shufflenet	shufflenet	NOUN
ajst-20810	162	34	groups	group	NOUN
ajst-20810	162	35	acc	acc	PROPN
ajst-20810	162	36	error	error	NOUN
ajst-20810	162	37	(	(	PUNCT
ajst-20810	162	38	%	%	INTJ
ajst-20810	162	39	,	,	PUNCT
ajst-20810	162	40	no	no	DET
ajst-20810	162	41	shuffle	shuffle	NOUN
ajst-20810	162	42	)	)	PUNCT
ajst-20810	162	43	acc	acc	NOUN
ajst-20810	162	44	error	error	NOUN
ajst-20810	162	45	(	(	PUNCT
ajst-20810	162	46	%	%	INTJ
ajst-20810	162	47	,	,	PUNCT
ajst-20810	162	48	shuffle	shuffle	NOUN
ajst-20810	162	49	)	)	PUNCT
ajst-20810	162	50	shufflenet(g=3	shufflenet(g=3	NUM
ajst-20810	162	51	)	)	PUNCT
ajst-20810	162	52	94.33	94.33	NUM
ajst-20810	162	53	32.6	32.6	NUM
ajst-20810	162	54	shufflenet(g=8	shufflenet(g=8	NOUN
ajst-20810	162	55	)	)	PUNCT
ajst-20810	162	56	94.35	94.35	NUM
ajst-20810	162	57	32.4	32.4	NUM
ajst-20810	162	58	from	from	ADP
ajst-20810	162	59	the	the	DET
ajst-20810	162	60	results	result	NOUN
ajst-20810	162	61	,	,	PUNCT
ajst-20810	162	62	it	it	PRON
ajst-20810	162	63	can	can	AUX
ajst-20810	162	64	be	be	AUX
ajst-20810	162	65	seen	see	VERB
ajst-20810	162	66	that	that	SCONJ
ajst-20810	162	67	when	when	SCONJ
ajst-20810	162	68	the	the	DET
ajst-20810	162	69	number	number	NOUN
ajst-20810	162	70	of	of	ADP
ajst-20810	162	71	groups	group	NOUN
ajst-20810	162	72	g	g	PROPN
ajst-20810	162	73	is	be	AUX
ajst-20810	162	74	3	3	NUM
ajst-20810	162	75	,	,	PUNCT
ajst-20810	162	76	the	the	DET
ajst-20810	162	77	overall	overall	ADJ
ajst-20810	162	78	accuracy	accuracy	NOUN
ajst-20810	162	79	of	of	ADP
ajst-20810	162	80	the	the	DET
ajst-20810	162	81	shufflenet	shufflenet	NOUN
ajst-20810	162	82	model	model	NOUN
ajst-20810	162	83	with	with	ADP
ajst-20810	162	84	channel	channel	NOUN
ajst-20810	162	85	shuffle	shuffle	NOUN
ajst-20810	162	86	is	be	AUX
ajst-20810	162	87	much	much	ADV
ajst-20810	162	88	higher	high	ADJ
ajst-20810	162	89	than	than	ADP
ajst-20810	162	90	that	that	PRON
ajst-20810	162	91	of	of	ADP
ajst-20810	162	92	the	the	DET
ajst-20810	162	93	shufflenet	shufflenet	NOUN
ajst-20810	162	94	model	model	NOUN
ajst-20810	162	95	without	without	ADP
ajst-20810	162	96	channel	channel	NOUN
ajst-20810	162	97	shuffle	shuffle	NOUN
ajst-20810	162	98	.	.	PUNCT
ajst-20810	163	1	this	this	PRON
ajst-20810	163	2	indicates	indicate	VERB
ajst-20810	163	3	that	that	SCONJ
ajst-20810	163	4	whether	whether	SCONJ
ajst-20810	163	5	or	or	CCONJ
ajst-20810	163	6	not	not	PART
ajst-20810	163	7	channel	channel	NOUN
ajst-20810	163	8	shuffle	shuffle	NOUN
ajst-20810	163	9	is	be	AUX
ajst-20810	163	10	performed	perform	VERB
ajst-20810	163	11	has	have	VERB
ajst-20810	163	12	a	a	DET
ajst-20810	163	13	great	great	ADJ
ajst-20810	163	14	impact	impact	NOUN
ajst-20810	163	15	on	on	ADP
ajst-20810	163	16	the	the	DET
ajst-20810	163	17	classification	classification	NOUN
ajst-20810	163	18	performance	performance	NOUN
ajst-20810	163	19	,	,	PUNCT
ajst-20810	163	20	and	and	CCONJ
ajst-20810	163	21	pure	pure	ADJ
ajst-20810	163	22	group	group	NOUN
ajst-20810	163	23	convolution	convolution	NOUN
ajst-20810	163	24	hinders	hinder	VERB
ajst-20810	163	25	the	the	DET
ajst-20810	163	26	interaction	interaction	NOUN
ajst-20810	163	27	of	of	ADP
ajst-20810	163	28	channel	channel	NOUN
ajst-20810	163	29	information	information	NOUN
ajst-20810	163	30	between	between	ADP
ajst-20810	163	31	groups	group	NOUN
ajst-20810	163	32	,	,	PUNCT
ajst-20810	163	33	so	so	ADV
ajst-20810	163	34	channel	channel	NOUN
ajst-20810	163	35	information	information	NOUN
ajst-20810	163	36	flows	flow	VERB
ajst-20810	163	37	better	well	ADV
ajst-20810	163	38	after	after	SCONJ
ajst-20810	163	39	channel	channel	NOUN
ajst-20810	163	40	shuffle	shuffle	NOUN
ajst-20810	163	41	is	be	AUX
ajst-20810	163	42	performed	perform	VERB
ajst-20810	163	43	,	,	PUNCT
ajst-20810	163	44	and	and	CCONJ
ajst-20810	163	45	the	the	DET
ajst-20810	163	46	classification	classification	NOUN
ajst-20810	163	47	performance	performance	NOUN
ajst-20810	163	48	is	be	AUX
ajst-20810	163	49	relatively	relatively	ADV
ajst-20810	163	50	better	well	ADJ
ajst-20810	163	51	.	.	PUNCT
ajst-20810	164	1	6	6	X
ajst-20810	164	2	.	.	X
ajst-20810	164	3	conclusion	conclusion	NOUN
ajst-20810	164	4	with	with	ADP
ajst-20810	164	5	the	the	DET
ajst-20810	164	6	arrival	arrival	NOUN
ajst-20810	164	7	of	of	ADP
ajst-20810	164	8	the	the	DET
ajst-20810	164	9	intelligent	intelligent	ADJ
ajst-20810	164	10	information	information	NOUN
ajst-20810	164	11	age	age	NOUN
ajst-20810	164	12	,	,	PUNCT
ajst-20810	164	13	computer	computer	NOUN
ajst-20810	164	14	hardware	hardware	NOUN
ajst-20810	164	15	is	be	AUX
ajst-20810	164	16	constantly	constantly	ADV
ajst-20810	164	17	upgraded	upgrade	VERB
ajst-20810	164	18	,	,	PUNCT
ajst-20810	164	19	software	software	NOUN
ajst-20810	164	20	algorithms	algorithm	NOUN
ajst-20810	164	21	are	be	AUX
ajst-20810	164	22	constantly	constantly	ADV
ajst-20810	164	23	updated	update	VERB
ajst-20810	164	24	,	,	PUNCT
ajst-20810	164	25	the	the	DET
ajst-20810	164	26	field	field	NOUN
ajst-20810	164	27	of	of	ADP
ajst-20810	164	28	artificial	artificial	ADJ
ajst-20810	164	29	intelligence	intelligence	NOUN
ajst-20810	164	30	is	be	AUX
ajst-20810	164	31	developing	develop	VERB
ajst-20810	164	32	rapidly	rapidly	ADV
ajst-20810	164	33	,	,	PUNCT
ajst-20810	164	34	and	and	CCONJ
ajst-20810	164	35	image	image	NOUN
ajst-20810	164	36	classification	classification	NOUN
ajst-20810	164	37	technology	technology	NOUN
ajst-20810	164	38	based	base	VERB
ajst-20810	164	39	on	on	ADP
ajst-20810	164	40	deep	deep	ADJ
ajst-20810	164	41	neural	neural	ADJ
ajst-20810	164	42	networks	network	NOUN
ajst-20810	164	43	has	have	AUX
ajst-20810	164	44	been	be	AUX
ajst-20810	164	45	more	more	ADV
ajst-20810	164	46	widely	widely	ADV
ajst-20810	164	47	used	use	VERB
ajst-20810	164	48	.	.	PUNCT
ajst-20810	165	1	this	this	DET
ajst-20810	165	2	paper	paper	NOUN
ajst-20810	165	3	reviews	review	VERB
ajst-20810	165	4	the	the	DET
ajst-20810	165	5	research	research	NOUN
ajst-20810	165	6	background	background	NOUN
ajst-20810	165	7	,	,	PUNCT
ajst-20810	165	8	significance	significance	NOUN
ajst-20810	165	9	and	and	CCONJ
ajst-20810	165	10	current	current	ADJ
ajst-20810	165	11	status	status	NOUN
ajst-20810	165	12	of	of	ADP
ajst-20810	165	13	convolutional	convolutional	ADJ
ajst-20810	165	14	neural	neural	ADJ
ajst-20810	165	15	network	network	NOUN
ajst-20810	165	16	and	and	CCONJ
ajst-20810	165	17	image	image	NOUN
ajst-20810	165	18	classification	classification	NOUN
ajst-20810	165	19	technology	technology	NOUN
ajst-20810	165	20	,	,	PUNCT
ajst-20810	165	21	and	and	CCONJ
ajst-20810	165	22	well	well	ADV
ajst-20810	165	23	implements	implement	VERB
ajst-20810	165	24	image	image	NOUN
ajst-20810	165	25	classification	classification	NOUN
ajst-20810	165	26	based	base	VERB
ajst-20810	165	27	on	on	ADP
ajst-20810	165	28	resnet	resnet	NOUN
ajst-20810	165	29	and	and	CCONJ
ajst-20810	165	30	image	image	NOUN
ajst-20810	165	31	classification	classification	NOUN
ajst-20810	165	32	based	base	VERB
ajst-20810	165	33	on	on	ADP
ajst-20810	165	34	shufflenet	shufflenet	NOUN
ajst-20810	165	35	,	,	PUNCT
ajst-20810	165	36	and	and	CCONJ
ajst-20810	165	37	carries	carry	VERB
ajst-20810	165	38	out	out	ADP
ajst-20810	165	39	comparative	comparative	ADJ
ajst-20810	165	40	experiments	experiment	NOUN
ajst-20810	165	41	on	on	ADP
ajst-20810	165	42	their	their	PRON
ajst-20810	165	43	classification	classification	NOUN
ajst-20810	165	44	performance	performance	NOUN
ajst-20810	165	45	,	,	PUNCT
ajst-20810	165	46	and	and	CCONJ
ajst-20810	165	47	investigates	investigate	VERB
ajst-20810	165	48	the	the	DET
ajst-20810	165	49	classification	classification	NOUN
ajst-20810	165	50	performance	performance	NOUN
ajst-20810	165	51	of	of	ADP
ajst-20810	165	52	image	image	NOUN
ajst-20810	165	53	classification	classification	NOUN
ajst-20810	165	54	based	base	VERB
ajst-20810	165	55	on	on	ADP
ajst-20810	165	56	resnet	resnet	NOUN
ajst-20810	165	57	in	in	ADP
ajst-20810	165	58	different	different	ADJ
ajst-20810	165	59	network	network	NOUN
ajst-20810	165	60	depths	depth	NOUN
ajst-20810	165	61	,	,	PUNCT
ajst-20810	165	62	and	and	CCONJ
ajst-20810	165	63	the	the	DET
ajst-20810	165	64	classification	classification	NOUN
ajst-20810	165	65	performance	performance	NOUN
ajst-20810	165	66	of	of	ADP
ajst-20810	165	67	image	image	NOUN
ajst-20810	165	68	classification	classification	NOUN
ajst-20810	165	69	based	base	VERB
ajst-20810	165	70	on	on	ADP
ajst-20810	165	71	shufflenet	shufflenet	NOUN
ajst-20810	165	72	in	in	ADP
ajst-20810	165	73	different	different	ADJ
ajst-20810	165	74	group	group	NOUN
ajst-20810	165	75	sizes	size	NOUN
ajst-20810	165	76	.	.	PUNCT
ajst-20810	166	1	classification	classification	NOUN
ajst-20810	166	2	in	in	ADP
ajst-20810	166	3	different	different	ADJ
ajst-20810	166	4	group	group	NOUN
ajst-20810	166	5	sizes	size	NOUN
ajst-20810	166	6	,	,	PUNCT
ajst-20810	166	7	and	and	CCONJ
ajst-20810	166	8	also	also	ADV
ajst-20810	166	9	verified	verify	VERB
ajst-20810	166	10	the	the	DET
ajst-20810	166	11	importance	importance	NOUN
ajst-20810	166	12	of	of	ADP
ajst-20810	166	13	channel	channel	NOUN
ajst-20810	166	14	shuffle	shuffle	NOUN
ajst-20810	166	15	in	in	ADP
ajst-20810	166	16	shufflenet	shufflenet	NOUN
ajst-20810	166	17	model	model	NOUN
ajst-20810	166	18	.	.	PUNCT
ajst-20810	167	1	acknowledgment	acknowledgment	NOUN
ajst-20810	167	2	this	this	DET
ajst-20810	167	3	work	work	NOUN
ajst-20810	167	4	is	be	AUX
ajst-20810	167	5	supported	support	VERB
ajst-20810	167	6	by	by	ADP
ajst-20810	167	7	the	the	DET
ajst-20810	167	8	natural	natural	ADJ
ajst-20810	167	9	science	science	NOUN
ajst-20810	167	10	fund	fund	NOUN
ajst-20810	167	11	for	for	ADP
ajst-20810	167	12	colleges	college	NOUN
ajst-20810	167	13	and	and	CCONJ
ajst-20810	167	14	universities	university	NOUN
ajst-20810	167	15	,	,	PUNCT
ajst-20810	167	16	department	department	NOUN
ajst-20810	167	17	of	of	ADP
ajst-20810	167	18	education	education	NOUN
ajst-20810	167	19	of	of	ADP
ajst-20810	167	20	anhui	anhui	PROPN
ajst-20810	167	21	province	province	PROPN
ajst-20810	167	22	(	(	PUNCT
ajst-20810	167	23	kj2021a0481	kj2021a0481	PROPN
ajst-20810	167	24	)	)	PUNCT
ajst-20810	167	25	,	,	PUNCT
ajst-20810	167	26	and	and	CCONJ
ajst-20810	167	27	anhui	anhui	PROPN
ajst-20810	167	28	university	university	PROPN
ajst-20810	167	29	of	of	ADP
ajst-20810	167	30	finance	finance	PROPN
ajst-20810	167	31	and	and	CCONJ
ajst-20810	167	32	economics	economic	NOUN
ajst-20810	167	33	graduate	graduate	NOUN
ajst-20810	167	34	student	student	NOUN
ajst-20810	167	35	research	research	NOUN
ajst-20810	167	36	and	and	CCONJ
ajst-20810	167	37	innovation	innovation	NOUN
ajst-20810	167	38	fund	fund	NOUN
ajst-20810	167	39	project	project	NOUN
ajst-20810	167	40	(	(	PUNCT
ajst-20810	167	41	acyc2023173	acyc2023173	PROPN
ajst-20810	167	42	)	)	PUNCT
ajst-20810	167	43	.	.	PUNCT
ajst-20810	168	1	references	reference	NOUN
ajst-20810	168	2	[	[	X
ajst-20810	168	3	1	1	NUM
ajst-20810	168	4	]	]	X
ajst-20810	168	5	lecun	lecun	PROPN
ajst-20810	168	6	y	y	PROPN
ajst-20810	168	7	,	,	PUNCT
ajst-20810	168	8	learning	learn	VERB
ajst-20810	168	9	invariant	invariant	ADJ
ajst-20810	168	10	feature	feature	NOUN
ajst-20810	168	11	hierarchies[c]//european	hierarchies[c]//european	ADJ
ajst-20810	168	12	conference	conference	NOUN
ajst-20810	168	13	on	on	ADP
ajst-20810	168	14	computer	computer	NOUN
ajst-20810	168	15	vision	vision	NOUN
ajst-20810	168	16	.	.	PUNCT
ajst-20810	169	1	springer	springer	NOUN
ajst-20810	169	2	,	,	PUNCT
ajst-20810	169	3	berlin	berlin	PROPN
ajst-20810	169	4	,	,	PUNCT
ajst-20810	169	5	heidelberg	heidelberg	PROPN
ajst-20810	169	6	,	,	PUNCT
ajst-20810	169	7	2012	2012	NUM
ajst-20810	169	8	:	:	PUNCT
ajst-20810	169	9	496	496	NUM
ajst-20810	169	10	-	-	SYM
ajst-20810	169	11	505	505	NUM
ajst-20810	169	12	.	.	PUNCT
ajst-20810	170	1	[	[	X
ajst-20810	170	2	2	2	NUM
ajst-20810	170	3	]	]	X
ajst-20810	170	4	deng	deng	PROPN
ajst-20810	170	5	i	i	PROPN
ajst-20810	170	6	,	,	PUNCT
ajst-20810	170	7	dong	dong	PROPN
ajst-20810	170	8	w	w	PROPN
ajst-20810	170	9	,	,	PUNCT
ajst-20810	170	10	socher	socher	NOUN
ajst-20810	170	11	r	r	NOUN
ajst-20810	170	12	,	,	PUNCT
ajst-20810	170	13	et	et	PROPN
ajst-20810	170	14	al	al	PROPN
ajst-20810	170	15	.	.	PUNCT
ajst-20810	170	16	image	image	PROPN
ajst-20810	170	17	net	net	NOUN
ajst-20810	170	18	:	:	PUNCT
ajst-20810	170	19	a	a	DET
ajst-20810	170	20	largescale	largescale	ADJ
ajst-20810	170	21	hierarchical	hierarchical	ADJ
ajst-20810	170	22	image	image	NOUN
ajst-20810	170	23	database[c]//computer	database[c]//computer	NOUN
ajst-20810	170	24	vision	vision	NOUN
ajst-20810	170	25	and	and	CCONJ
ajst-20810	170	26	pattern	pattern	NOUN
ajst-20810	170	27	recognition	recognition	NOUN
ajst-20810	170	28	.	.	PUNCT
ajst-20810	171	1	ieee	ieee	PROPN
ajst-20810	171	2	conference	conference	PROPN
ajst-20810	171	3	on	on	ADP
ajst-20810	171	4	.	.	PUNCT
ajst-20810	172	1	ieee	ieee	PROPN
ajst-20810	172	2	,	,	PUNCT
ajst-20810	172	3	2009	2009	NUM
ajst-20810	172	4	:	:	PUNCT
ajst-20810	172	5	248	248	NUM
ajst-20810	172	6	-	-	SYM
ajst-20810	172	7	255	255	NUM
ajst-20810	172	8	.	.	PUNCT
ajst-20810	173	1	184	184	NUM
ajst-20810	173	2	[	[	X
ajst-20810	173	3	3	3	NUM
ajst-20810	173	4	]	]	X
ajst-20810	173	5	krizhevsky	krizhevsky	NOUN
ajst-20810	173	6	a	a	PROPN
ajst-20810	173	7	,	,	PUNCT
ajst-20810	173	8	sutskever	sutskever	VERB
ajst-20810	173	9	i	i	PRON
ajst-20810	173	10	,	,	PUNCT
ajst-20810	173	11	hinton	hinton	PROPN
ajst-20810	173	12	g	g	PROPN
ajst-20810	173	13	e.	e.	PROPN
ajst-20810	173	14	imagenet	imagenet	PROPN
ajst-20810	173	15	classification	classification	NOUN
ajst-20810	173	16	with	with	ADP
ajst-20810	173	17	deep	deep	ADJ
ajst-20810	173	18	convolutional	convolutional	ADJ
ajst-20810	173	19	neural	neural	ADJ
ajst-20810	173	20	networks[j	networks[j	NOUN
ajst-20810	173	21	]	]	X
ajst-20810	173	22	.	.	PUNCT
ajst-20810	174	1	communications	communication	NOUN
ajst-20810	174	2	of	of	ADP
ajst-20810	174	3	the	the	DET
ajst-20810	174	4	acm	acm	NOUN
ajst-20810	174	5	,	,	PUNCT
ajst-20810	174	6	2017.60(6	2017.60(6	NUM
ajst-20810	174	7	):	):	PUNCT
ajst-20810	174	8	84	84	NUM
ajst-20810	174	9	-	-	SYM
ajst-20810	174	10	90	90	NUM
ajst-20810	174	11	.	.	PUNCT
ajst-20810	175	1	[	[	X
ajst-20810	175	2	4	4	NUM
ajst-20810	175	3	]	]	X
ajst-20810	175	4	lecun	lecun	PROPN
ajst-20810	175	5	y	y	PROPN
ajst-20810	175	6	,	,	PUNCT
ajst-20810	175	7	bottou	bottou	PROPN
ajst-20810	175	8	l	l	PROPN
ajst-20810	175	9	,	,	PUNCT
ajst-20810	175	10	bengio	bengio	PROPN
ajst-20810	175	11	y	y	PROPN
ajst-20810	175	12	,	,	PUNCT
ajst-20810	175	13	et	et	PROPN
ajst-20810	175	14	al	al	PROPN
ajst-20810	175	15	.	.	PUNCT
ajst-20810	175	16	gradient	gradient	NOUN
ajst-20810	175	17	-	-	PUNCT
ajst-20810	175	18	based	base	VERB
ajst-20810	175	19	learning	learning	NOUN
ajst-20810	175	20	applied	apply	VERB
ajst-20810	175	21	to	to	ADP
ajst-20810	175	22	document	document	NOUN
ajst-20810	175	23	recognition[j	recognition[j	NOUN
ajst-20810	175	24	]	]	PUNCT
ajst-20810	175	25	.	.	PUNCT
ajst-20810	176	1	pro	pro	ADJ
ajst-20810	176	2	-	-	NOUN
ajst-20810	176	3	ceedings	ceeding	NOUN
ajst-20810	176	4	of	of	ADP
ajst-20810	176	5	the	the	DET
ajst-20810	176	6	ieee	ieee	NOUN
ajst-20810	176	7	,	,	PUNCT
ajst-20810	176	8	1998	1998	NUM
ajst-20810	176	9	,	,	PUNCT
ajst-20810	176	10	86(11	86(11	NUM
ajst-20810	176	11	):	):	PUNCT
ajst-20810	176	12	2278	2278	NUM
ajst-20810	176	13	-	-	SYM
ajst-20810	176	14	2324	2324	NUM
ajst-20810	176	15	.	.	PUNCT
ajst-20810	177	1	[	[	X
ajst-20810	177	2	5	5	X
ajst-20810	177	3	]	]	PUNCT
ajst-20810	177	4	szegedy	szegedy	NOUN
ajst-20810	177	5	c	c	PROPN
ajst-20810	177	6	,	,	PUNCT
ajst-20810	177	7	wei	wei	PROPN
ajst-20810	177	8	l	l	PROPN
ajst-20810	177	9	,	,	PUNCT
ajst-20810	177	10	et	et	PROPN
ajst-20810	177	11	al	al	PROPN
ajst-20810	177	12	.	.	PUNCT
ajst-20810	178	1	going	go	VERB
ajst-20810	178	2	deeper	deeply	ADV
ajst-20810	178	3	with	with	ADP
ajst-20810	178	4	convolutions[c]//2015	convolutions[c]//2015	PROPN
ajst-20810	178	5	ieee	ieee	NOUN
ajst-20810	178	6	conference	conference	NOUN
ajst-20810	178	7	on	on	ADP
ajst-20810	178	8	computer	computer	NOUN
ajst-20810	178	9	vision	vision	NOUN
ajst-20810	178	10	and	and	CCONJ
ajst-20810	178	11	pattern	pattern	NOUN
ajst-20810	178	12	recognition	recognition	NOUN
ajst-20810	178	13	(	(	PUNCT
ajst-20810	178	14	cvpr	cvpr	NOUN
ajst-20810	178	15	)	)	PUNCT
ajst-20810	178	16	.	.	PUNCT
ajst-20810	179	1	ieee	ieee	PROPN
ajst-20810	179	2	,	,	PUNCT
ajst-20810	179	3	2015	2015	NUM
ajst-20810	179	4	,	,	PUNCT
ajst-20810	179	5	24(1):205	24(1):205	NOUN
ajst-20810	179	6	-	-	PUNCT
ajst-20810	179	7	211	211	NUM
ajst-20810	179	8	.	.	PUNCT
ajst-20810	180	1	[	[	X
ajst-20810	180	2	6	6	NUM
ajst-20810	180	3	]	]	SYM
ajst-20810	180	4	simonyan	simonyan	PROPN
ajst-20810	180	5	k	k	NOUN
ajst-20810	180	6	,	,	PUNCT
ajst-20810	180	7	zisserman	zisserman	NOUN
ajst-20810	180	8	a.	a.	NOUN
ajst-20810	180	9	very	very	ADV
ajst-20810	180	10	deep	deep	ADJ
ajst-20810	180	11	convolutional	convolutional	ADJ
ajst-20810	180	12	networks	network	NOUN
ajst-20810	180	13	for	for	ADP
ajst-20810	180	14	large	large	ADJ
ajst-20810	180	15	-	-	PUNCT
ajst-20810	180	16	scale	scale	NOUN
ajst-20810	180	17	image	image	NOUN
ajst-20810	180	18	recognition[j	recognition[j	NOUN
ajst-20810	180	19	]	]	PUNCT
ajst-20810	180	20	.	.	PUNCT
ajst-20810	181	1	computer	computer	NOUN
ajst-20810	181	2	science	science	NOUN
ajst-20810	181	3	,	,	PUNCT
ajst-20810	181	4	2014	2014	NUM
ajst-20810	181	5	.	.	PUNCT
ajst-20810	182	1	36(1):231	36(1):231	NUM
ajst-20810	182	2	-	-	SYM
ajst-20810	182	3	235	235	NUM
ajst-20810	182	4	.	.	PUNCT
ajst-20810	183	1	[	[	X
ajst-20810	183	2	7	7	X
ajst-20810	183	3	]	]	X
ajst-20810	183	4	larochelle	larochelle	PROPN
ajst-20810	183	5	h	h	PROPN
ajst-20810	183	6	,	,	PUNCT
ajst-20810	183	7	mandel	mandel	PROPN
ajst-20810	183	8	m	m	PROPN
ajst-20810	183	9	,	,	PUNCT
ajst-20810	183	10	pascanu	pascanu	PROPN
ajst-20810	183	11	r	r	NOUN
ajst-20810	183	12	,	,	PUNCT
ajst-20810	183	13	et	et	PROPN
ajst-20810	183	14	al	al	PROPN
ajst-20810	183	15	.	.	PUNCT
ajst-20810	183	16	leaming	leame	VERB
ajst-20810	183	17	algorithms	algorithm	NOUN
ajst-20810	183	18	for	for	ADP
ajst-20810	183	19	the	the	DET
ajst-20810	183	20	classification	classification	NOUN
ajst-20810	183	21	restricted	restrict	VERB
ajst-20810	183	22	boltzmann	boltzmann	PROPN
ajst-20810	183	23	machine[j	machine[j	PROPN
ajst-20810	183	24	]	]	PUNCT
ajst-20810	183	25	.	.	PUNCT
ajst-20810	184	1	the	the	DET
ajst-20810	184	2	journal	journal	NOUN
ajst-20810	184	3	of	of	ADP
ajst-20810	184	4	machine	machine	NOUN
ajst-20810	184	5	learning	learn	VERB
ajst-20810	184	6	research	research	NOUN
ajst-20810	184	7	,	,	PUNCT
ajst-20810	184	8	2012	2012	NUM
ajst-20810	184	9	,	,	PUNCT
ajst-20810	184	10	13(1	13(1	NUM
ajst-20810	184	11	):	):	PUNCT
ajst-20810	184	12	643	643	NUM
ajst-20810	184	13	-	-	SYM
ajst-20810	184	14	669	669	NUM
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ajst-20810	185	1	[	[	X
ajst-20810	185	2	8	8	NUM
ajst-20810	185	3	]	]	PUNCT
ajst-20810	185	4	he	he	PRON
ajst-20810	185	5	k	k	PROPN
ajst-20810	185	6	,	,	PUNCT
ajst-20810	185	7	zhang	zhang	PROPN
ajst-20810	185	8	x	x	PROPN
ajst-20810	185	9	,	,	PUNCT
ajst-20810	185	10	ren	ren	PROPN
ajst-20810	185	11	s	s	PROPN
ajst-20810	185	12	,	,	PUNCT
ajst-20810	185	13	et	et	PROPN
ajst-20810	185	14	al	al	PROPN
ajst-20810	185	15	.	.	PUNCT
ajst-20810	186	1	deep	deep	ADJ
ajst-20810	186	2	residual	residual	ADJ
ajst-20810	186	3	learning	learning	NOUN
ajst-20810	186	4	for	for	ADP
ajst-20810	186	5	image	image	NOUN
ajst-20810	186	6	recognition[j	recognition[j	NOUN
ajst-20810	186	7	]	]	PUNCT
ajst-20810	186	8	.	.	PUNCT
ajst-20810	187	1	ieee	ieee	NOUN
ajst-20810	187	2	,	,	PUNCT
ajst-20810	187	3	2016:770	2016:770	NUM
ajst-20810	187	4	-	-	SYM
ajst-20810	187	5	778	778	NUM
ajst-20810	187	6	.	.	PUNCT
ajst-20810	188	1	[	[	X
ajst-20810	188	2	9	9	NUM
ajst-20810	188	3	]	]	X
ajst-20810	188	4	zhang	zhang	PROPN
ajst-20810	188	5	x	x	PROPN
ajst-20810	188	6	,	,	PUNCT
ajst-20810	188	7	zhou	zhou	PROPN
ajst-20810	188	8	x	x	PROPN
ajst-20810	188	9	,	,	PUNCT
ajst-20810	188	10	lin	lin	PROPN
ajst-20810	188	11	m	m	PROPN
ajst-20810	188	12	,	,	PUNCT
ajst-20810	188	13	et	et	PROPN
ajst-20810	188	14	al	al	PROPN
ajst-20810	188	15	.	.	PROPN
ajst-20810	188	16	shufflenet	shufflenet	PROPN
ajst-20810	188	17	:	:	PUNCT
ajst-20810	188	18	an	an	DET
ajst-20810	188	19	extremely	extremely	ADV
ajst-20810	188	20	efficient	efficient	ADJ
ajst-20810	188	21	convolutional	convolutional	ADJ
ajst-20810	188	22	neural	neural	ADJ
ajst-20810	188	23	network	network	NOUN
ajst-20810	188	24	for	for	ADP
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ajst-20810	188	26	devices[j	devices[j	PROPN
ajst-20810	188	27	]	]	PUNCT
ajst-20810	188	28	.	.	PUNCT
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ajst-20810	189	2	.	.	PUNCT
ajst-20810	190	1	[	[	X
ajst-20810	190	2	10	10	NUM
ajst-20810	190	3	]	]	X
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ajst-20810	190	5	r	r	NOUN
ajst-20810	190	6	,	,	PUNCT
ajst-20810	190	7	zisserman	zisserman	NOUN
ajst-20810	190	8	a.	a.	NOUN
ajst-20810	190	9	all	all	ADV
ajst-20810	190	10	about	about	ADP
ajst-20810	190	11	vlad[c]//proceedings	vlad[c]//proceeding	NOUN
ajst-20810	190	12	of	of	ADP
ajst-20810	190	13	the	the	DET
ajst-20810	190	14	ieee	ieee	NOUN
ajst-20810	190	15	conference	conference	NOUN
ajst-20810	190	16	on	on	ADP
ajst-20810	190	17	computer	computer	NOUN
ajst-20810	190	18	vision	vision	NOUN
ajst-20810	190	19	and	and	CCONJ
ajst-20810	190	20	pattern	pattern	NOUN
ajst-20810	190	21	recognition.2013:1578	recognition.2013:1578	NOUN
ajst-20810	190	22	-	-	PUNCT
ajst-20810	190	23	1585	1585	NUM
ajst-20810	190	24	.	.	PUNCT
ajst-20810	191	1	[	[	X
ajst-20810	191	2	11	11	NUM
ajst-20810	191	3	]	]	PUNCT
ajst-20810	191	4	sanchez	sanchez	PROPN
ajst-20810	191	5	j	j	PROPN
ajst-20810	191	6	,	,	PUNCT
ajst-20810	191	7	perronnin	perronnin	ADJ
ajst-20810	191	8	f	f	NUM
ajst-20810	191	9	,	,	PUNCT
ajst-20810	191	10	mensink	mensink	PROPN
ajst-20810	191	11	t	t	PROPN
ajst-20810	191	12	,	,	PUNCT
ajst-20810	191	13	et	et	PROPN
ajst-20810	191	14	al	al	PROPN
ajst-20810	191	15	.	.	PUNCT
ajst-20810	191	16	image	image	NOUN
ajst-20810	191	17	classification	classification	NOUN
ajst-20810	191	18	with	with	ADP
ajst-20810	191	19	the	the	DET
ajst-20810	191	20	fisher	fisher	PROPN
ajst-20810	191	21	vector	vector	PROPN
ajst-20810	191	22	:	:	PUNCT
ajst-20810	191	23	theory	theory	NOUN
ajst-20810	191	24	and	and	CCONJ
ajst-20810	191	25	practice[j	practice[j	PROPN
ajst-20810	191	26	]	]	PUNCT
ajst-20810	191	27	.	.	PUNCT
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ajst-20810	192	2	journal	journal	PROPN
ajst-20810	192	3	of	of	ADP
ajst-20810	192	4	computer	computer	NOUN
ajst-20810	192	5	vision	vision	NOUN
ajst-20810	192	6	,	,	PUNCT
ajst-20810	192	7	2013	2013	NUM
ajst-20810	192	8	,	,	PUNCT
ajst-20810	192	9	105(3	105(3	NUM
ajst-20810	192	10	):	):	PUNCT
ajst-20810	192	11	222	222	NUM
ajst-20810	192	12	-	-	SYM
ajst-20810	192	13	245	245	NUM
ajst-20810	192	14	.	.	PUNCT
ajst-20810	193	1	[	[	X
ajst-20810	193	2	12	12	NUM
ajst-20810	193	3	]	]	X
ajst-20810	193	4	srivastava	srivastava	PROPN
ajst-20810	193	5	r	r	PROPN
ajst-20810	193	6	k	k	PROPN
ajst-20810	193	7	,	,	PUNCT
ajst-20810	193	8	greff	greff	PROPN
ajst-20810	193	9	k	k	PROPN
ajst-20810	193	10	,	,	PUNCT
ajst-20810	193	11	schmidhuber	schmidhuber	PROPN
ajst-20810	193	12	j.	j.	PROPN
ajst-20810	193	13	highway	highway	PROPN
ajst-20810	193	14	networks[j	networks[j	PROPN
ajst-20810	193	15	]	]	PUNCT
ajst-20810	193	16	.	.	PUNCT
ajst-20810	194	1	arxiv	arxiv	PROPN
ajst-20810	194	2	preprint	preprint	VERB
ajst-20810	194	3	arxiv:1505.00387,2015	arxiv:1505.00387,2015	PROPN
ajst-20810	194	4	.	.	PUNCT