id	sid	tid	token	lemma	pos
fcis-2969	1	1	frontiers	frontier	NOUN
fcis-2969	1	2	in	in	ADP
fcis-2969	1	3	computing	computing	NOUN
fcis-2969	1	4	and	and	CCONJ
fcis-2969	1	5	intelligent	intelligent	ADJ
fcis-2969	1	6	systems	system	NOUN
fcis-2969	1	7	issn	issn	VERB
fcis-2969	1	8	:	:	PUNCT
fcis-2969	1	9	2832	2832	NUM
fcis-2969	1	10	-	-	SYM
fcis-2969	1	11	6024	6024	NUM
fcis-2969	1	12	|	|	NOUN
fcis-2969	1	13	vol	vol	NOUN
fcis-2969	1	14	.	.	PROPN
fcis-2969	2	1	2	2	NUM
fcis-2969	2	2	,	,	PUNCT
fcis-2969	2	3	no	no	INTJ
fcis-2969	2	4	.	.	NOUN
fcis-2969	2	5	1	1	NUM
fcis-2969	2	6	,	,	PUNCT
fcis-2969	2	7	2022	2022	NUM
fcis-2969	2	8	67	67	NUM
fcis-2969	2	9	a	a	DET
fcis-2969	2	10	review	review	NOUN
fcis-2969	2	11	of	of	ADP
fcis-2969	2	12	facial	facial	ADJ
fcis-2969	2	13	expression	expression	NOUN
fcis-2969	2	14	recognition	recognition	NOUN
fcis-2969	2	15	jianghai	jianghai	PROPN
fcis-2969	2	16	lan1,2	lan1,2	PROPN
fcis-2969	2	17	,	,	PUNCT
fcis-2969	2	18	guojun	guojun	PROPN
fcis-2969	2	19	lin	lin	PROPN
fcis-2969	2	20	1,2	1,2	NUM
fcis-2969	2	21	*	*	SYM
fcis-2969	2	22	1	1	NUM
fcis-2969	2	23	artificial	artificial	ADJ
fcis-2969	2	24	intelligence	intelligence	NOUN
fcis-2969	2	25	key	key	NOUN
fcis-2969	2	26	laboratory	laboratory	NOUN
fcis-2969	2	27	of	of	ADP
fcis-2969	2	28	sichuan	sichuan	PROPN
fcis-2969	2	29	province	province	PROPN
fcis-2969	2	30	,	,	PUNCT
fcis-2969	2	31	sichuan	sichuan	PROPN
fcis-2969	2	32	university	university	PROPN
fcis-2969	2	33	of	of	ADP
fcis-2969	2	34	science	science	NOUN
fcis-2969	2	35	and	and	CCONJ
fcis-2969	2	36	engineering	engineering	NOUN
fcis-2969	2	37	,	,	PUNCT
fcis-2969	2	38	zigong	zigong	PROPN
fcis-2969	2	39	643000	643000	NUM
fcis-2969	2	40	,	,	PUNCT
fcis-2969	2	41	china	china	PROPN
fcis-2969	2	42	2	2	NUM
fcis-2969	2	43	school	school	NOUN
fcis-2969	2	44	of	of	ADP
fcis-2969	2	45	automation	automation	NOUN
fcis-2969	2	46	and	and	CCONJ
fcis-2969	2	47	information	information	NOUN
fcis-2969	2	48	engineering	engineering	NOUN
fcis-2969	2	49	,	,	PUNCT
fcis-2969	2	50	sichuan	sichuan	PROPN
fcis-2969	2	51	university	university	PROPN
fcis-2969	2	52	of	of	ADP
fcis-2969	2	53	science	science	NOUN
fcis-2969	2	54	and	and	CCONJ
fcis-2969	2	55	engineering	engineering	NOUN
fcis-2969	2	56	,	,	PUNCT
fcis-2969	2	57	zigong	zigong	PROPN
fcis-2969	2	58	643000	643000	NUM
fcis-2969	2	59	,	,	PUNCT
fcis-2969	2	60	china	china	PROPN
fcis-2969	2	61	*	*	PUNCT
fcis-2969	2	62	corresponding	correspond	VERB
fcis-2969	2	63	author	author	NOUN
fcis-2969	2	64	:	:	PUNCT
fcis-2969	2	65	guojun	guojun	PROPN
fcis-2969	2	66	lin	lin	PROPN
fcis-2969	2	67	(	(	PUNCT
fcis-2969	2	68	email	email	NOUN
fcis-2969	2	69	:	:	PUNCT
fcis-2969	2	70	386988463@qq.com	386988463@qq.com	NUM
fcis-2969	2	71	)	)	PUNCT
fcis-2969	2	72	abstract	abstract	NOUN
fcis-2969	2	73	:	:	PUNCT
fcis-2969	2	74	with	with	ADP
fcis-2969	2	75	the	the	DET
fcis-2969	2	76	development	development	NOUN
fcis-2969	2	77	of	of	ADP
fcis-2969	2	78	deep	deep	ADJ
fcis-2969	2	79	learning	learning	NOUN
fcis-2969	2	80	,	,	PUNCT
fcis-2969	2	81	deep	deep	ADJ
fcis-2969	2	82	learning	learning	NOUN
fcis-2969	2	83	is	be	AUX
fcis-2969	2	84	becoming	become	VERB
fcis-2969	2	85	more	more	ADV
fcis-2969	2	86	and	and	CCONJ
fcis-2969	2	87	more	more	ADV
fcis-2969	2	88	common	common	ADJ
fcis-2969	2	89	for	for	ADP
fcis-2969	2	90	facial	facial	ADJ
fcis-2969	2	91	recognition	recognition	NOUN
fcis-2969	2	92	.	.	PUNCT
fcis-2969	3	1	we	we	PRON
fcis-2969	3	2	summarize	summarize	VERB
fcis-2969	3	3	some	some	DET
fcis-2969	3	4	widely	widely	ADV
fcis-2969	3	5	used	use	VERB
fcis-2969	3	6	public	public	ADJ
fcis-2969	3	7	data	datum	NOUN
fcis-2969	3	8	sets	set	NOUN
fcis-2969	3	9	for	for	ADP
fcis-2969	3	10	facial	facial	ADJ
fcis-2969	3	11	expression	expression	NOUN
fcis-2969	3	12	recognition	recognition	NOUN
fcis-2969	3	13	;	;	PUNCT
fcis-2969	3	14	the	the	DET
fcis-2969	3	15	basic	basic	ADJ
fcis-2969	3	16	flow	flow	NOUN
fcis-2969	3	17	of	of	ADP
fcis-2969	3	18	facial	facial	ADJ
fcis-2969	3	19	expression	expression	NOUN
fcis-2969	3	20	recognition	recognition	NOUN
fcis-2969	3	21	is	be	AUX
fcis-2969	3	22	briefly	briefly	ADV
fcis-2969	3	23	introduced	introduce	VERB
fcis-2969	3	24	.	.	PUNCT
fcis-2969	4	1	this	this	DET
fcis-2969	4	2	paper	paper	NOUN
fcis-2969	4	3	mainly	mainly	ADV
fcis-2969	4	4	analyzes	analyze	VERB
fcis-2969	4	5	some	some	DET
fcis-2969	4	6	existing	exist	VERB
fcis-2969	4	7	deep	deep	ADJ
fcis-2969	4	8	learning	learning	NOUN
fcis-2969	4	9	methods	method	NOUN
fcis-2969	4	10	,	,	PUNCT
fcis-2969	4	11	especially	especially	ADV
fcis-2969	4	12	deep	deep	ADJ
fcis-2969	4	13	convolutional	convolutional	ADJ
fcis-2969	4	14	neural	neural	ADJ
fcis-2969	4	15	network	network	NOUN
fcis-2969	4	16	.	.	PUNCT
fcis-2969	5	1	the	the	DET
fcis-2969	5	2	structure	structure	NOUN
fcis-2969	5	3	analysis	analysis	NOUN
fcis-2969	5	4	and	and	CCONJ
fcis-2969	5	5	performance	performance	NOUN
fcis-2969	5	6	comparison	comparison	NOUN
fcis-2969	5	7	of	of	ADP
fcis-2969	5	8	four	four	NUM
fcis-2969	5	9	classical	classical	ADJ
fcis-2969	5	10	convolutional	convolutional	ADJ
fcis-2969	5	11	neural	neural	ADJ
fcis-2969	5	12	networks	network	NOUN
fcis-2969	5	13	(	(	PUNCT
fcis-2969	5	14	alexnet	alexnet	ADJ
fcis-2969	5	15	,	,	PUNCT
fcis-2969	5	16	googlenet	googlenet	NOUN
fcis-2969	5	17	,	,	PUNCT
fcis-2969	5	18	vggnet	vggnet	NOUN
fcis-2969	5	19	and	and	CCONJ
fcis-2969	5	20	resnet	resnet	NOUN
fcis-2969	5	21	)	)	PUNCT
fcis-2969	5	22	are	be	AUX
fcis-2969	5	23	carried	carry	VERB
fcis-2969	5	24	out	out	ADP
fcis-2969	5	25	.	.	PUNCT
fcis-2969	6	1	finally	finally	ADV
fcis-2969	6	2	,	,	PUNCT
fcis-2969	6	3	the	the	DET
fcis-2969	6	4	present	present	ADJ
fcis-2969	6	5	research	research	NOUN
fcis-2969	6	6	on	on	ADP
fcis-2969	6	7	expression	expression	NOUN
fcis-2969	6	8	recognition	recognition	NOUN
fcis-2969	6	9	is	be	AUX
fcis-2969	6	10	summarized	summarize	VERB
fcis-2969	6	11	and	and	CCONJ
fcis-2969	6	12	prospected	prospect	VERB
fcis-2969	6	13	.	.	PUNCT
fcis-2969	7	1	keywords	keyword	NOUN
fcis-2969	7	2	:	:	PUNCT
fcis-2969	7	3	expression	expression	NOUN
fcis-2969	7	4	recognition	recognition	NOUN
fcis-2969	7	5	;	;	PUNCT
fcis-2969	7	6	deep	deep	ADJ
fcis-2969	7	7	learning	learning	NOUN
fcis-2969	7	8	;	;	PUNCT
fcis-2969	7	9	feature	feature	NOUN
fcis-2969	7	10	extraction	extraction	NOUN
fcis-2969	7	11	.	.	PUNCT
fcis-2969	8	1	1	1	X
fcis-2969	8	2	.	.	X
fcis-2969	8	3	introduction	introduction	NOUN
fcis-2969	8	4	people	people	NOUN
fcis-2969	8	5	can	can	AUX
fcis-2969	8	6	convey	convey	VERB
fcis-2969	8	7	a	a	DET
fcis-2969	8	8	lot	lot	NOUN
fcis-2969	8	9	of	of	ADP
fcis-2969	8	10	rich	rich	ADJ
fcis-2969	8	11	information	information	NOUN
fcis-2969	8	12	through	through	ADP
fcis-2969	8	13	facial	facial	ADJ
fcis-2969	8	14	expressions	expression	NOUN
fcis-2969	8	15	,	,	PUNCT
fcis-2969	8	16	which	which	PRON
fcis-2969	8	17	play	play	VERB
fcis-2969	8	18	an	an	DET
fcis-2969	8	19	important	important	ADJ
fcis-2969	8	20	role	role	NOUN
fcis-2969	8	21	in	in	ADP
fcis-2969	8	22	the	the	DET
fcis-2969	8	23	communication	communication	NOUN
fcis-2969	8	24	between	between	ADP
fcis-2969	8	25	people	people	NOUN
fcis-2969	8	26	.	.	PUNCT
fcis-2969	9	1	for	for	ADP
fcis-2969	9	2	example	example	NOUN
fcis-2969	9	3	,	,	PUNCT
fcis-2969	9	4	a	a	DET
fcis-2969	9	5	look	look	NOUN
fcis-2969	9	6	in	in	ADP
fcis-2969	9	7	the	the	DET
fcis-2969	9	8	teacher	teacher	NOUN
fcis-2969	9	9	's	's	PART
fcis-2969	9	10	eyes	eye	NOUN
fcis-2969	9	11	in	in	ADP
fcis-2969	9	12	class	class	NOUN
fcis-2969	9	13	can	can	AUX
fcis-2969	9	14	make	make	VERB
fcis-2969	9	15	naughty	naughty	ADJ
fcis-2969	9	16	students	student	NOUN
fcis-2969	9	17	correct	correct	VERB
fcis-2969	9	18	attitude	attitude	NOUN
fcis-2969	9	19	;	;	PUNCT
fcis-2969	9	20	a	a	DET
fcis-2969	9	21	lot	lot	NOUN
fcis-2969	9	22	of	of	ADP
fcis-2969	9	23	situations	situation	NOUN
fcis-2969	9	24	in	in	ADP
fcis-2969	9	25	movies	movie	NOUN
fcis-2969	9	26	also	also	ADV
fcis-2969	9	27	use	use	VERB
fcis-2969	9	28	people	people	NOUN
fcis-2969	9	29	's	's	PART
fcis-2969	9	30	expressions	expression	NOUN
fcis-2969	9	31	to	to	PART
fcis-2969	9	32	convey	convey	VERB
fcis-2969	9	33	information	information	NOUN
fcis-2969	9	34	;	;	PUNCT
fcis-2969	9	35	there	there	PRON
fcis-2969	9	36	is	be	VERB
fcis-2969	9	37	the	the	DET
fcis-2969	9	38	annual	annual	ADJ
fcis-2969	9	39	spring	spring	NOUN
fcis-2969	9	40	festival	festival	NOUN
fcis-2969	9	41	gala	gala	NOUN
fcis-2969	9	42	to	to	PART
fcis-2969	9	43	see	see	VERB
fcis-2969	9	44	the	the	DET
fcis-2969	9	45	sketch	sketch	NOUN
fcis-2969	9	46	,	,	PUNCT
fcis-2969	9	47	the	the	DET
fcis-2969	9	48	actors	actor	NOUN
fcis-2969	9	49	on	on	ADP
fcis-2969	9	50	the	the	DET
fcis-2969	9	51	stage	stage	NOUN
fcis-2969	9	52	through	through	ADP
fcis-2969	9	53	its	its	PRON
fcis-2969	9	54	exaggerated	exaggerated	ADJ
fcis-2969	9	55	facial	facial	ADJ
fcis-2969	9	56	expressions	expression	NOUN
fcis-2969	9	57	let	let	VERB
fcis-2969	9	58	us	we	PRON
fcis-2969	9	59	smile	smile	VERB
fcis-2969	9	60	from	from	ADP
fcis-2969	9	61	ear	ear	NOUN
fcis-2969	9	62	to	to	ADP
fcis-2969	9	63	ear	ear	NOUN
fcis-2969	9	64	.	.	PUNCT
fcis-2969	10	1	there	there	PRON
fcis-2969	10	2	are	be	VERB
fcis-2969	10	3	many	many	ADJ
fcis-2969	10	4	applications	application	NOUN
fcis-2969	10	5	,	,	PUNCT
fcis-2969	10	6	in	in	ADP
fcis-2969	10	7	short	short	ADJ
fcis-2969	10	8	,	,	PUNCT
fcis-2969	10	9	the	the	DET
fcis-2969	10	10	study	study	NOUN
fcis-2969	10	11	of	of	ADP
fcis-2969	10	12	human	human	ADJ
fcis-2969	10	13	facial	facial	ADJ
fcis-2969	10	14	expression	expression	NOUN
fcis-2969	10	15	has	have	VERB
fcis-2969	10	16	a	a	DET
fcis-2969	10	17	lot	lot	NOUN
fcis-2969	10	18	of	of	ADP
fcis-2969	10	19	value	value	NOUN
fcis-2969	10	20	,	,	PUNCT
fcis-2969	10	21	can	can	AUX
fcis-2969	10	22	be	be	AUX
fcis-2969	10	23	applied	apply	VERB
fcis-2969	10	24	to	to	ADP
fcis-2969	10	25	many	many	ADJ
fcis-2969	10	26	aspects	aspect	NOUN
fcis-2969	10	27	.	.	PUNCT
fcis-2969	11	1	this	this	DET
fcis-2969	11	2	paper	paper	NOUN
fcis-2969	11	3	mainly	mainly	ADV
fcis-2969	11	4	analyzes	analyze	VERB
fcis-2969	11	5	facial	facial	ADJ
fcis-2969	11	6	expression	expression	NOUN
fcis-2969	11	7	recognition	recognition	NOUN
fcis-2969	11	8	through	through	ADP
fcis-2969	11	9	the	the	DET
fcis-2969	11	10	deep	deep	ADJ
fcis-2969	11	11	learning	learning	NOUN
fcis-2969	11	12	method	method	NOUN
fcis-2969	11	13	.	.	PUNCT
fcis-2969	12	1	common	common	ADJ
fcis-2969	12	2	data	data	NOUN
fcis-2969	12	3	sets	set	NOUN
fcis-2969	12	4	are	be	AUX
fcis-2969	12	5	shown	show	VERB
fcis-2969	12	6	in	in	ADP
fcis-2969	12	7	table	table	NOUN
fcis-2969	12	8	1	1	NUM
fcis-2969	12	9	.	.	PUNCT
fcis-2969	12	10	table	table	NOUN
fcis-2969	12	11	1	1	NUM
fcis-2969	12	12	.	.	PUNCT
fcis-2969	12	13	common	common	ADJ
fcis-2969	12	14	data	datum	NOUN
fcis-2969	12	15	sets	set	VERB
fcis-2969	12	16	the	the	DET
fcis-2969	12	17	data	datum	NOUN
fcis-2969	12	18	set	set	VERB
fcis-2969	12	19	release	release	NOUN
fcis-2969	12	20	time	time	NOUN
fcis-2969	12	21	sample	sample	NOUN
fcis-2969	12	22	size	size	NOUN
fcis-2969	12	23	the	the	DET
fcis-2969	12	24	expression	expression	NOUN
fcis-2969	12	25	distribution	distribution	NOUN
fcis-2969	12	26	jaffe	jaffe	PROPN
fcis-2969	12	27	1998	1998	NUM
fcis-2969	12	28	213	213	NUM
fcis-2969	12	29	6	6	NUM
fcis-2969	12	30	basic	basic	ADJ
fcis-2969	12	31	expressions	expression	NOUN
fcis-2969	12	32	+1	+1	PRON
fcis-2969	12	33	neutral	neutral	ADJ
fcis-2969	12	34	ck+	ck+	NOUN
fcis-2969	12	35	2012	2012	NUM
fcis-2969	12	36	981	981	NUM
fcis-2969	12	37	6	6	NUM
fcis-2969	12	38	basic	basic	ADJ
fcis-2969	12	39	expressions	expression	NOUN
fcis-2969	12	40	+1	+1	PROPN
fcis-2969	12	41	neutral	neutral	ADJ
fcis-2969	12	42	fer2013	fer2013	ADJ
fcis-2969	12	43	2013	2013	NUM
fcis-2969	12	44	35886	35886	NUM
fcis-2969	12	45	6	6	NUM
fcis-2969	12	46	basic	basic	ADJ
fcis-2969	12	47	expressions	expression	NOUN
fcis-2969	12	48	+1	+1	PRON
fcis-2969	12	49	neutral	neutral	ADJ
fcis-2969	12	50	rafd	rafd	VERB
fcis-2969	12	51	2010	2010	NUM
fcis-2969	12	52	8040	8040	NUM
fcis-2969	12	53	7	7	NUM
fcis-2969	12	54	emoticons	emoticon	NOUN
fcis-2969	12	55	plus	plus	CCONJ
fcis-2969	12	56	1	1	NUM
fcis-2969	12	57	neutral	neutral	ADJ
fcis-2969	12	58	2	2	NUM
fcis-2969	12	59	.	.	PUNCT
fcis-2969	12	60	expression	expression	NOUN
fcis-2969	12	61	recognition	recognition	NOUN
fcis-2969	12	62	method	method	VERB
fcis-2969	12	63	facial	facial	ADJ
fcis-2969	12	64	expression	expression	NOUN
fcis-2969	12	65	recognition	recognition	NOUN
fcis-2969	12	66	is	be	AUX
fcis-2969	12	67	mainly	mainly	ADV
fcis-2969	12	68	divided	divide	VERB
fcis-2969	12	69	into	into	ADP
fcis-2969	12	70	four	four	NUM
fcis-2969	12	71	parts	part	NOUN
fcis-2969	12	72	:	:	PUNCT
fcis-2969	12	73	data	datum	NOUN
fcis-2969	12	74	acquisition	acquisition	NOUN
fcis-2969	12	75	,	,	PUNCT
fcis-2969	12	76	image	image	NOUN
fcis-2969	12	77	preprocessing	preprocessing	NOUN
fcis-2969	12	78	,	,	PUNCT
fcis-2969	12	79	feature	feature	NOUN
fcis-2969	12	80	extraction	extraction	NOUN
fcis-2969	12	81	and	and	CCONJ
fcis-2969	12	82	expression	expression	NOUN
fcis-2969	12	83	classification	classification	NOUN
fcis-2969	12	84	.	.	PUNCT
fcis-2969	13	1	the	the	DET
fcis-2969	13	2	flow	flow	NOUN
fcis-2969	13	3	chart	chart	NOUN
fcis-2969	13	4	is	be	AUX
fcis-2969	13	5	shown	show	VERB
fcis-2969	13	6	in	in	ADP
fcis-2969	13	7	figure	figure	NOUN
fcis-2969	13	8	1	1	NUM
fcis-2969	13	9	.	.	PUNCT
fcis-2969	13	10	feature	feature	NOUN
fcis-2969	13	11	extraction	extraction	NOUN
fcis-2969	13	12	is	be	AUX
fcis-2969	13	13	the	the	DET
fcis-2969	13	14	most	most	ADV
fcis-2969	13	15	critical	critical	ADJ
fcis-2969	13	16	link	link	NOUN
fcis-2969	13	17	,	,	PUNCT
fcis-2969	13	18	and	and	CCONJ
fcis-2969	13	19	the	the	DET
fcis-2969	13	20	effect	effect	NOUN
fcis-2969	13	21	of	of	ADP
fcis-2969	13	22	feature	feature	NOUN
fcis-2969	13	23	extraction	extraction	NOUN
fcis-2969	13	24	directly	directly	ADV
fcis-2969	13	25	affects	affect	VERB
fcis-2969	13	26	the	the	DET
fcis-2969	13	27	accuracy	accuracy	NOUN
fcis-2969	13	28	of	of	ADP
fcis-2969	13	29	the	the	DET
fcis-2969	13	30	final	final	ADJ
fcis-2969	13	31	expression	expression	NOUN
fcis-2969	13	32	recognition	recognition	NOUN
fcis-2969	13	33	.	.	PUNCT
fcis-2969	14	1	with	with	ADP
fcis-2969	14	2	the	the	DET
fcis-2969	14	3	rise	rise	NOUN
fcis-2969	14	4	of	of	ADP
fcis-2969	14	5	deep	deep	ADJ
fcis-2969	14	6	learning	learning	NOUN
fcis-2969	14	7	,	,	PUNCT
fcis-2969	14	8	facial	facial	ADJ
fcis-2969	14	9	expression	expression	NOUN
fcis-2969	14	10	feature	feature	NOUN
fcis-2969	14	11	extraction	extraction	NOUN
fcis-2969	14	12	through	through	ADP
fcis-2969	14	13	deep	deep	ADJ
fcis-2969	14	14	learning	learning	NOUN
fcis-2969	14	15	is	be	AUX
fcis-2969	14	16	becoming	become	VERB
fcis-2969	14	17	more	more	ADV
fcis-2969	14	18	and	and	CCONJ
fcis-2969	14	19	more	more	ADV
fcis-2969	14	20	widespread	widespread	ADJ
fcis-2969	14	21	.	.	PUNCT
fcis-2969	15	1	some	some	DET
fcis-2969	15	2	deep	deep	ADJ
fcis-2969	15	3	learning	learning	NOUN
fcis-2969	15	4	networks	network	NOUN
fcis-2969	15	5	are	be	AUX
fcis-2969	15	6	introduced	introduce	VERB
fcis-2969	15	7	below	below	ADV
fcis-2969	15	8	.	.	PUNCT
fcis-2969	16	1	figure	figure	NOUN
fcis-2969	16	2	1	1	NUM
fcis-2969	16	3	.	.	PUNCT
fcis-2969	16	4	facial	facial	ADJ
fcis-2969	16	5	expression	expression	NOUN
fcis-2969	16	6	recognition	recognition	NOUN
fcis-2969	16	7	process	process	NOUN
fcis-2969	16	8	2.1	2.1	NUM
fcis-2969	16	9	.	.	PUNCT
fcis-2969	17	1	deep	deep	ADJ
fcis-2969	17	2	belief	belief	NOUN
fcis-2969	17	3	network	network	NOUN
fcis-2969	17	4	the	the	DET
fcis-2969	17	5	concept	concept	NOUN
fcis-2969	17	6	of	of	ADP
fcis-2969	17	7	deep	deep	ADJ
fcis-2969	17	8	belief	belief	NOUN
fcis-2969	17	9	network	network	NOUN
fcis-2969	17	10	(	(	PUNCT
fcis-2969	17	11	dbn	dbn	PROPN
fcis-2969	17	12	)	)	PUNCT
fcis-2969	17	13	was	be	AUX
fcis-2969	17	14	put	put	VERB
fcis-2969	17	15	forward	forward	ADV
fcis-2969	17	16	by	by	ADP
fcis-2969	17	17	hinton	hinton	PROPN
fcis-2969	17	18	et	et	PROPN
fcis-2969	17	19	al	al	PROPN
fcis-2969	17	20	.	.	PUNCT
fcis-2969	18	1	[	[	X
fcis-2969	18	2	1	1	X
fcis-2969	18	3	]	]	PUNCT
fcis-2969	18	4	in	in	ADP
fcis-2969	18	5	2006	2006	NUM
fcis-2969	18	6	.	.	PUNCT
fcis-2969	19	1	dbn	dbn	PROPN
fcis-2969	19	2	is	be	AUX
fcis-2969	19	3	a	a	DET
fcis-2969	19	4	special	special	ADJ
fcis-2969	19	5	kind	kind	NOUN
fcis-2969	19	6	of	of	ADP
fcis-2969	19	7	neural	neural	ADJ
fcis-2969	19	8	network	network	NOUN
fcis-2969	19	9	.	.	PUNCT
fcis-2969	20	1	generally	generally	ADV
fcis-2969	20	2	,	,	PUNCT
fcis-2969	20	3	it	it	PRON
fcis-2969	20	4	is	be	AUX
fcis-2969	20	5	composed	compose	VERB
fcis-2969	20	6	of	of	ADP
fcis-2969	20	7	restricted	restrict	VERB
fcis-2969	20	8	boltamann	boltamann	NOUN
fcis-2969	20	9	machine	machine	NOUN
fcis-2969	20	10	(	(	PUNCT
fcis-2969	20	11	rbm	rbm	PROPN
fcis-2969	20	12	)	)	PUNCT
fcis-2969	20	13	connected	connect	VERB
fcis-2969	20	14	in	in	ADP
fcis-2969	20	15	series	series	NOUN
fcis-2969	20	16	and	and	CCONJ
fcis-2969	20	17	back	back	ADJ
fcis-2969	20	18	propagation	propagation	NOUN
fcis-2969	20	19	(	(	PUNCT
fcis-2969	20	20	bp	bp	PROPN
fcis-2969	20	21	)	)	PUNCT
fcis-2969	20	22	neural	neural	ADJ
fcis-2969	20	23	network	network	NOUN
fcis-2969	20	24	.	.	PUNCT
fcis-2969	21	1	on	on	ADP
fcis-2969	21	2	the	the	DET
fcis-2969	21	3	basis	basis	NOUN
fcis-2969	21	4	of	of	ADP
fcis-2969	21	5	dbn	dbn	PROPN
fcis-2969	21	6	,	,	PUNCT
fcis-2969	21	7	boosted	boost	VERB
fcis-2969	21	8	deep	deep	ADJ
fcis-2969	21	9	belief	belief	NOUN
fcis-2969	21	10	network	network	NOUN
fcis-2969	21	11	(	(	PUNCT
fcis-2969	21	12	bdbn	bdbn	NOUN
fcis-2969	21	13	)	)	PUNCT
fcis-2969	21	14	was	be	AUX
fcis-2969	21	15	proposed	propose	VERB
fcis-2969	21	16	by	by	ADP
fcis-2969	21	17	ping	ping	PROPN
fcis-2969	21	18	liu	liu	PROPN
fcis-2969	21	19	et	et	PROPN
fcis-2969	21	20	al	al	PROPN
fcis-2969	21	21	in	in	ADP
fcis-2969	21	22	2014	2014	NUM
fcis-2969	21	23	.	.	PUNCT
fcis-2969	22	1	features	feature	NOUN
fcis-2969	22	2	are	be	AUX
fcis-2969	22	3	jointly	jointly	ADV
fcis-2969	22	4	fine	fine	ADV
fcis-2969	22	5	-	-	PUNCT
fcis-2969	22	6	tuned	tune	VERB
fcis-2969	22	7	and	and	CCONJ
fcis-2969	22	8	selected	select	VERB
fcis-2969	22	9	to	to	PART
fcis-2969	22	10	form	form	VERB
fcis-2969	22	11	a	a	DET
fcis-2969	22	12	strong	strong	ADJ
fcis-2969	22	13	classifier	classifier	NOUN
fcis-2969	22	14	in	in	ADP
fcis-2969	22	15	a	a	DET
fcis-2969	22	16	new	new	ADJ
fcis-2969	22	17	enhanced	enhanced	ADJ
fcis-2969	22	18	top	top	ADJ
fcis-2969	22	19	-	-	PUNCT
fcis-2969	22	20	down	down	ADP
fcis-2969	22	21	supervised	supervised	ADJ
fcis-2969	22	22	feature	feature	NOUN
fcis-2969	22	23	enhancement	enhancement	NOUN
fcis-2969	22	24	(	(	PUNCT
fcis-2969	22	25	btd	btd	PROPN
fcis-2969	22	26	-	-	PUNCT
fcis-2969	22	27	sfs	sfs	ADJ
fcis-2969	22	28	)	)	PUNCT
fcis-2969	22	29	process	process	NOUN
fcis-2969	22	30	,	,	PUNCT
fcis-2969	22	31	through	through	ADP
fcis-2969	22	32	which	which	PRON
fcis-2969	22	33	highly	highly	ADV
fcis-2969	22	34	complex	complex	ADJ
fcis-2969	22	35	features	feature	NOUN
fcis-2969	22	36	can	can	AUX
fcis-2969	22	37	be	be	AUX
fcis-2969	22	38	learned	learn	VERB
fcis-2969	22	39	from	from	ADP
fcis-2969	22	40	facial	facial	ADJ
fcis-2969	22	41	images	image	NOUN
fcis-2969	22	42	.	.	PUNCT
fcis-2969	23	1	literature	literature	NOUN
fcis-2969	24	1	[	[	X
fcis-2969	24	2	2	2	X
fcis-2969	24	3	]	]	PUNCT
fcis-2969	24	4	proposed	propose	VERB
fcis-2969	24	5	an	an	DET
fcis-2969	24	6	au	au	ADV
fcis-2969	24	7	-	-	PUNCT
fcis-2969	24	8	inspired	inspire	VERB
fcis-2969	24	9	deep	deep	ADJ
fcis-2969	24	10	networks	network	NOUN
fcis-2969	24	11	(	(	PUNCT
fcis-2969	24	12	audn	audn	NOUN
fcis-2969	24	13	)	)	PUNCT
fcis-2969	24	14	composed	compose	VERB
fcis-2969	24	15	of	of	ADP
fcis-2969	24	16	three	three	NUM
fcis-2969	24	17	sequential	sequential	ADJ
fcis-2969	24	18	modules	module	NOUN
fcis-2969	24	19	,	,	PUNCT
fcis-2969	24	20	which	which	PRON
fcis-2969	24	21	achieved	achieve	VERB
fcis-2969	24	22	the	the	DET
fcis-2969	24	23	best	good	ADJ
fcis-2969	24	24	results	result	NOUN
fcis-2969	24	25	in	in	ADP
fcis-2969	24	26	the	the	DET
fcis-2969	24	27	experiments	experiment	NOUN
fcis-2969	24	28	on	on	ADP
fcis-2969	24	29	ck	ck	PROPN
fcis-2969	24	30	+	+	NOUN
fcis-2969	24	31	,	,	PUNCT
fcis-2969	24	32	mmi	mmi	NOUN
fcis-2969	24	33	and	and	CCONJ
fcis-2969	24	34	sfew	sfew	ADJ
fcis-2969	24	35	databases	database	NOUN
fcis-2969	24	36	.	.	PUNCT
fcis-2969	25	1	2.2	2.2	NUM
fcis-2969	25	2	.	.	PUNCT
fcis-2969	25	3	automatic	automatic	ADJ
fcis-2969	25	4	encoder	encoder	NOUN
fcis-2969	25	5	method	method	NOUN
fcis-2969	25	6	in	in	ADP
fcis-2969	25	7	1986	1986	NUM
fcis-2969	25	8	,	,	PUNCT
fcis-2969	25	9	rumelhart	rumelhart	PROPN
fcis-2969	25	10	proposed	propose	VERB
fcis-2969	25	11	the	the	DET
fcis-2969	25	12	concept	concept	NOUN
fcis-2969	25	13	of	of	ADP
fcis-2969	25	14	autoencoders	autoencoder	NOUN
fcis-2969	25	15	,	,	PUNCT
fcis-2969	25	16	which	which	PRON
fcis-2969	25	17	can	can	AUX
fcis-2969	25	18	extract	extract	VERB
fcis-2969	25	19	the	the	DET
fcis-2969	25	20	implicit	implicit	ADJ
fcis-2969	25	21	features	feature	NOUN
fcis-2969	25	22	of	of	ADP
fcis-2969	25	23	data	datum	NOUN
fcis-2969	25	24	and	and	CCONJ
fcis-2969	25	25	learn	learn	VERB
fcis-2969	25	26	to	to	PART
fcis-2969	25	27	reconstruct	reconstruct	VERB
fcis-2969	25	28	the	the	DET
fcis-2969	25	29	data	datum	NOUN
fcis-2969	25	30	with	with	ADP
fcis-2969	25	31	these	these	DET
fcis-2969	25	32	features	feature	NOUN
fcis-2969	25	33	.	.	PUNCT
fcis-2969	26	1	early	early	ADJ
fcis-2969	26	2	autoencoders	autoencoder	NOUN
fcis-2969	26	3	were	be	AUX
fcis-2969	26	4	used	use	VERB
fcis-2969	26	5	in	in	ADP
fcis-2969	26	6	data	data	NOUN
fcis-2969	26	7	compression	compression	NOUN
fcis-2969	26	8	and	and	CCONJ
fcis-2969	26	9	data	datum	NOUN
fcis-2969	26	10	processing	processing	NOUN
fcis-2969	26	11	,	,	PUNCT
fcis-2969	26	12	but	but	CCONJ
fcis-2969	26	13	the	the	DET
fcis-2969	26	14	compression	compression	NOUN
fcis-2969	26	15	effect	effect	NOUN
fcis-2969	26	16	largely	largely	ADV
fcis-2969	26	17	depended	depend	VERB
fcis-2969	26	18	on	on	ADP
fcis-2969	26	19	the	the	DET
fcis-2969	26	20	data	data	NOUN
fcis-2969	26	21	compression	compression	NOUN
fcis-2969	26	22	68	68	NUM
fcis-2969	26	23	itself	itself	PRON
fcis-2969	26	24	,	,	PUNCT
fcis-2969	26	25	and	and	CCONJ
fcis-2969	26	26	there	there	PRON
fcis-2969	26	27	would	would	AUX
fcis-2969	26	28	be	be	AUX
fcis-2969	26	29	data	data	NOUN
fcis-2969	26	30	loss	loss	NOUN
fcis-2969	26	31	.	.	PUNCT
fcis-2969	27	1	literature[3	literature[3	PUNCT
fcis-2969	27	2	]	]	PUNCT
fcis-2969	27	3	describes	describe	VERB
fcis-2969	27	4	the	the	DET
fcis-2969	27	5	conversion	conversion	NOUN
fcis-2969	27	6	of	of	ADP
fcis-2969	27	7	high	high	ADJ
fcis-2969	27	8	-	-	PUNCT
fcis-2969	27	9	dimensional	dimensional	ADJ
fcis-2969	27	10	data	datum	NOUN
fcis-2969	27	11	into	into	ADP
fcis-2969	27	12	low	low	ADJ
fcis-2969	27	13	-	-	PUNCT
fcis-2969	27	14	dimensional	dimensional	ADJ
fcis-2969	27	15	data	datum	NOUN
fcis-2969	27	16	by	by	ADP
fcis-2969	27	17	deep	deep	ADJ
fcis-2969	27	18	auto	auto	NOUN
fcis-2969	27	19	encoder	encoder	NOUN
fcis-2969	27	20	(	(	PUNCT
fcis-2969	27	21	dae	dae	VERB
fcis-2969	27	22	)	)	PUNCT
fcis-2969	27	23	.	.	PUNCT
fcis-2969	28	1	the	the	DET
fcis-2969	28	2	idea	idea	NOUN
fcis-2969	28	3	of	of	ADP
fcis-2969	28	4	dae	dae	NOUN
fcis-2969	28	5	is	be	AUX
fcis-2969	28	6	to	to	PART
fcis-2969	28	7	train	train	VERB
fcis-2969	28	8	the	the	DET
fcis-2969	28	9	whole	whole	ADJ
fcis-2969	28	10	model	model	NOUN
fcis-2969	28	11	layer	layer	NOUN
fcis-2969	28	12	by	by	ADP
fcis-2969	28	13	layer	layer	NOUN
fcis-2969	28	14	in	in	ADP
fcis-2969	28	15	pre	pre	ADJ
fcis-2969	28	16	-	-	NOUN
fcis-2969	28	17	training	training	NOUN
fcis-2969	28	18	.	.	PUNCT
fcis-2969	29	1	compared	compare	VERB
fcis-2969	29	2	with	with	ADP
fcis-2969	29	3	the	the	DET
fcis-2969	29	4	automatic	automatic	ADJ
fcis-2969	29	5	encoder	encoder	NOUN
fcis-2969	29	6	,	,	PUNCT
fcis-2969	29	7	dae	dae	NOUN
fcis-2969	29	8	is	be	AUX
fcis-2969	29	9	optimized	optimize	VERB
fcis-2969	29	10	to	to	PART
fcis-2969	29	11	reconstruct	reconstruct	VERB
fcis-2969	29	12	its	its	PRON
fcis-2969	29	13	input	input	NOUN
fcis-2969	29	14	with	with	ADP
fcis-2969	29	15	as	as	ADP
fcis-2969	29	16	low	low	ADJ
fcis-2969	29	17	reconstruction	reconstruction	NOUN
fcis-2969	29	18	error	error	NOUN
fcis-2969	29	19	as	as	ADP
fcis-2969	29	20	possible	possible	ADJ
fcis-2969	29	21	.	.	PUNCT
fcis-2969	30	1	literature[3	literature[3	PUNCT
fcis-2969	30	2	]	]	PUNCT
fcis-2969	30	3	puts	put	VERB
fcis-2969	30	4	forward	forward	ADV
fcis-2969	30	5	a	a	DET
fcis-2969	30	6	new	new	ADJ
fcis-2969	30	7	facial	facial	ADJ
fcis-2969	30	8	expression	expression	NOUN
fcis-2969	30	9	recognition	recognition	NOUN
fcis-2969	30	10	method	method	NOUN
fcis-2969	30	11	using	use	VERB
fcis-2969	30	12	dsae	dsae	NOUN
fcis-2969	30	13	,	,	PUNCT
fcis-2969	30	14	which	which	PRON
fcis-2969	30	15	combines	combine	VERB
fcis-2969	30	16	geometric	geometric	ADJ
fcis-2969	30	17	features	feature	NOUN
fcis-2969	30	18	and	and	CCONJ
fcis-2969	30	19	appearance	appearance	NOUN
fcis-2969	30	20	features	feature	VERB
fcis-2969	30	21	to	to	PART
fcis-2969	30	22	recognize	recognize	VERB
fcis-2969	30	23	expressions	expression	NOUN
fcis-2969	30	24	automatically	automatically	ADV
fcis-2969	30	25	and	and	CCONJ
fcis-2969	30	26	accurately	accurately	ADV
fcis-2969	30	27	.	.	PUNCT
fcis-2969	31	1	2.3	2.3	NUM
fcis-2969	31	2	.	.	PUNCT
fcis-2969	32	1	deep	deep	ADJ
fcis-2969	32	2	convolutional	convolutional	ADJ
fcis-2969	32	3	neural	neural	ADJ
fcis-2969	32	4	network	network	NOUN
fcis-2969	32	5	convolutional	convolutional	ADJ
fcis-2969	32	6	neural	neural	ADJ
fcis-2969	32	7	networks	network	NOUN
fcis-2969	32	8	(	(	PUNCT
fcis-2969	32	9	cnn	cnn	PROPN
fcis-2969	32	10	)	)	PUNCT
fcis-2969	32	11	are	be	AUX
fcis-2969	32	12	generally	generally	ADV
fcis-2969	32	13	composed	compose	VERB
fcis-2969	32	14	of	of	ADP
fcis-2969	32	15	three	three	NUM
fcis-2969	32	16	processing	processing	NOUN
fcis-2969	32	17	layers	layer	NOUN
fcis-2969	32	18	:	:	PUNCT
fcis-2969	32	19	convolutional	convolutional	ADJ
fcis-2969	32	20	layer	layer	NOUN
fcis-2969	32	21	,	,	PUNCT
fcis-2969	32	22	pooling	pool	VERB
fcis-2969	32	23	layer	layer	NOUN
fcis-2969	32	24	and	and	CCONJ
fcis-2969	32	25	full	full	ADJ
fcis-2969	32	26	-	-	PUNCT
fcis-2969	32	27	link	link	NOUN
fcis-2969	32	28	layer	layer	NOUN
fcis-2969	32	29	.	.	PUNCT
fcis-2969	33	1	the	the	DET
fcis-2969	33	2	function	function	NOUN
fcis-2969	33	3	of	of	ADP
fcis-2969	33	4	the	the	DET
fcis-2969	33	5	convolution	convolution	NOUN
fcis-2969	33	6	layer	layer	NOUN
fcis-2969	33	7	is	be	AUX
fcis-2969	33	8	to	to	PART
fcis-2969	33	9	extract	extract	VERB
fcis-2969	33	10	the	the	DET
fcis-2969	33	11	feature	feature	NOUN
fcis-2969	33	12	of	of	ADP
fcis-2969	33	13	the	the	DET
fcis-2969	33	14	image	image	NOUN
fcis-2969	33	15	.	.	PUNCT
fcis-2969	34	1	the	the	DET
fcis-2969	34	2	pooling	pool	VERB
fcis-2969	34	3	layer	layer	NOUN
fcis-2969	34	4	carries	carry	VERB
fcis-2969	34	5	out	out	ADP
fcis-2969	34	6	sparse	sparse	ADJ
fcis-2969	34	7	processing	processing	NOUN
fcis-2969	34	8	on	on	ADP
fcis-2969	34	9	the	the	DET
fcis-2969	34	10	feature	feature	NOUN
fcis-2969	34	11	image	image	NOUN
fcis-2969	34	12	to	to	PART
fcis-2969	34	13	reduce	reduce	VERB
fcis-2969	34	14	the	the	DET
fcis-2969	34	15	computational	computational	ADJ
fcis-2969	34	16	load	load	NOUN
fcis-2969	34	17	(	(	PUNCT
fcis-2969	34	18	dimensionality	dimensionality	NOUN
fcis-2969	34	19	reduction	reduction	NOUN
fcis-2969	34	20	)	)	PUNCT
fcis-2969	34	21	.	.	PUNCT
fcis-2969	35	1	in	in	ADP
fcis-2969	35	2	the	the	DET
fcis-2969	35	3	fully	fully	ADV
fcis-2969	35	4	linked	link	VERB
fcis-2969	35	5	layer	layer	NOUN
fcis-2969	35	6	,	,	PUNCT
fcis-2969	35	7	the	the	DET
fcis-2969	35	8	neurons	neuron	NOUN
fcis-2969	35	9	of	of	ADP
fcis-2969	35	10	each	each	DET
fcis-2969	35	11	layer	layer	NOUN
fcis-2969	35	12	are	be	AUX
fcis-2969	35	13	connected	connect	VERB
fcis-2969	35	14	with	with	ADP
fcis-2969	35	15	all	all	DET
fcis-2969	35	16	the	the	DET
fcis-2969	35	17	neurons	neuron	NOUN
fcis-2969	35	18	in	in	ADP
fcis-2969	35	19	the	the	DET
fcis-2969	35	20	subsequent	subsequent	ADJ
fcis-2969	35	21	layer	layer	NOUN
fcis-2969	35	22	,	,	PUNCT
fcis-2969	35	23	and	and	CCONJ
fcis-2969	35	24	whether	whether	SCONJ
fcis-2969	35	25	the	the	DET
fcis-2969	35	26	neurons	neuron	NOUN
fcis-2969	35	27	are	be	AUX
fcis-2969	35	28	triggered	trigger	VERB
fcis-2969	35	29	is	be	AUX
fcis-2969	35	30	determined	determine	VERB
fcis-2969	35	31	by	by	ADP
fcis-2969	35	32	the	the	DET
fcis-2969	35	33	sum	sum	NOUN
fcis-2969	35	34	of	of	ADP
fcis-2969	35	35	the	the	DET
fcis-2969	35	36	input	input	NOUN
fcis-2969	35	37	weights	weight	NOUN
fcis-2969	35	38	of	of	ADP
fcis-2969	35	39	the	the	DET
fcis-2969	35	40	connected	connected	ADJ
fcis-2969	35	41	neurons	neuron	NOUN
fcis-2969	35	42	.	.	PUNCT
fcis-2969	36	1	the	the	DET
fcis-2969	36	2	unique	unique	ADJ
fcis-2969	36	3	local	local	ADJ
fcis-2969	36	4	connection	connection	NOUN
fcis-2969	36	5	and	and	CCONJ
fcis-2969	36	6	weight	weight	NOUN
fcis-2969	36	7	sharing	sharing	NOUN
fcis-2969	36	8	of	of	ADP
fcis-2969	36	9	cnn	cnn	PROPN
fcis-2969	36	10	are	be	AUX
fcis-2969	36	11	not	not	PART
fcis-2969	36	12	found	find	VERB
fcis-2969	36	13	in	in	ADP
fcis-2969	36	14	other	other	ADJ
fcis-2969	36	15	neural	neural	ADJ
fcis-2969	36	16	networks	network	NOUN
fcis-2969	36	17	.	.	PUNCT
fcis-2969	37	1	this	this	PRON
fcis-2969	37	2	makes	make	VERB
fcis-2969	37	3	cnn	cnn	PROPN
fcis-2969	37	4	network	network	PROPN
fcis-2969	37	5	less	less	ADJ
fcis-2969	37	6	parameters	parameter	NOUN
fcis-2969	37	7	,	,	PUNCT
fcis-2969	37	8	higher	high	ADJ
fcis-2969	37	9	efficiency	efficiency	NOUN
fcis-2969	37	10	,	,	PUNCT
fcis-2969	37	11	better	well	ADJ
fcis-2969	37	12	regularization	regularization	NOUN
fcis-2969	37	13	effect	effect	NOUN
fcis-2969	37	14	and	and	CCONJ
fcis-2969	37	15	so	so	ADV
fcis-2969	37	16	on	on	ADV
fcis-2969	37	17	.	.	PUNCT
fcis-2969	38	1	classical	classical	ADJ
fcis-2969	38	2	networks	network	NOUN
fcis-2969	38	3	based	base	VERB
fcis-2969	38	4	on	on	ADP
fcis-2969	38	5	cnn	cnn	PROPN
fcis-2969	38	6	include	include	VERB
fcis-2969	38	7	alexnet	alexnet	ADJ
fcis-2969	38	8	,	,	PUNCT
fcis-2969	38	9	googlenet	googlenet	NOUN
fcis-2969	38	10	,	,	PUNCT
fcis-2969	38	11	vggnet	vggnet	NOUN
fcis-2969	38	12	,	,	PUNCT
fcis-2969	38	13	resnet	resnet	NOUN
fcis-2969	38	14	,	,	PUNCT
fcis-2969	38	15	etc	etc	X
fcis-2969	38	16	.	.	X
fcis-2969	39	1	alexnet[4	alexnet[4	PROPN
fcis-2969	39	2	]	]	PUNCT
fcis-2969	39	3	was	be	AUX
fcis-2969	39	4	proposed	propose	VERB
fcis-2969	39	5	in	in	ADP
fcis-2969	39	6	2012	2012	NUM
fcis-2969	39	7	by	by	ADP
fcis-2969	39	8	alex	alex	PROPN
fcis-2969	39	9	krizhevsky	krizhevsky	PROPN
fcis-2969	39	10	,	,	PUNCT
fcis-2969	39	11	ilya	ilya	PROPN
fcis-2969	39	12	sutskever	sutskever	VERB
fcis-2969	39	13	and	and	CCONJ
fcis-2969	39	14	geoffrey	geoffrey	PROPN
fcis-2969	39	15	hinton	hinton	PROPN
fcis-2969	39	16	et	et	PROPN
fcis-2969	39	17	al	al	PROPN
fcis-2969	39	18	.	.	PUNCT
fcis-2969	39	19	alexnet	alexnet	PROPN
fcis-2969	39	20	has	have	VERB
fcis-2969	39	21	5	5	NUM
fcis-2969	39	22	convolution	convolution	NOUN
fcis-2969	39	23	layers	layer	NOUN
fcis-2969	39	24	and	and	CCONJ
fcis-2969	39	25	3	3	NUM
fcis-2969	39	26	fully	fully	ADV
fcis-2969	39	27	connected	connected	ADJ
fcis-2969	39	28	layers	layer	NOUN
fcis-2969	39	29	.	.	PUNCT
fcis-2969	40	1	the	the	DET
fcis-2969	40	2	structure	structure	NOUN
fcis-2969	40	3	of	of	ADP
fcis-2969	40	4	the	the	DET
fcis-2969	40	5	first	first	ADJ
fcis-2969	40	6	two	two	NUM
fcis-2969	40	7	alexnet	alexnet	ADJ
fcis-2969	40	8	convolution	convolution	NOUN
fcis-2969	40	9	layers	layer	NOUN
fcis-2969	40	10	is	be	AUX
fcis-2969	40	11	similar	similar	ADJ
fcis-2969	40	12	.	.	PUNCT
fcis-2969	41	1	first	first	ADV
fcis-2969	41	2	,	,	PUNCT
fcis-2969	41	3	a	a	DET
fcis-2969	41	4	convolution	convolution	NOUN
fcis-2969	41	5	layer	layer	NOUN
fcis-2969	41	6	is	be	AUX
fcis-2969	41	7	followed	follow	VERB
fcis-2969	41	8	by	by	ADP
fcis-2969	41	9	a	a	DET
fcis-2969	41	10	relu	relu	NOUN
fcis-2969	41	11	activation	activation	NOUN
fcis-2969	41	12	function	function	NOUN
fcis-2969	41	13	layer	layer	NOUN
fcis-2969	41	14	,	,	PUNCT
fcis-2969	41	15	then	then	ADV
fcis-2969	41	16	a	a	DET
fcis-2969	41	17	pooling	pool	VERB
fcis-2969	41	18	layer	layer	NOUN
fcis-2969	41	19	,	,	PUNCT
fcis-2969	41	20	and	and	CCONJ
fcis-2969	41	21	finally	finally	ADV
fcis-2969	41	22	an	an	DET
fcis-2969	41	23	lrn	lrn	PROPN
fcis-2969	41	24	layer	layer	NOUN
fcis-2969	41	25	.	.	PUNCT
fcis-2969	42	1	but	but	CCONJ
fcis-2969	42	2	the	the	DET
fcis-2969	42	3	parameters	parameter	NOUN
fcis-2969	42	4	of	of	ADP
fcis-2969	42	5	the	the	DET
fcis-2969	42	6	two	two	NUM
fcis-2969	42	7	convolution	convolution	NOUN
fcis-2969	42	8	layers	layer	NOUN
fcis-2969	42	9	are	be	AUX
fcis-2969	42	10	different	different	ADJ
fcis-2969	42	11	.	.	PUNCT
fcis-2969	43	1	the	the	DET
fcis-2969	43	2	last	last	ADJ
fcis-2969	43	3	three	three	NUM
fcis-2969	43	4	convolution	convolution	NOUN
fcis-2969	43	5	layers	layer	NOUN
fcis-2969	43	6	of	of	ADP
fcis-2969	43	7	alexnet	alexnet	NOUN
fcis-2969	43	8	are	be	AUX
fcis-2969	43	9	followed	follow	VERB
fcis-2969	43	10	by	by	ADP
fcis-2969	43	11	a	a	DET
fcis-2969	43	12	relu	relu	NOUN
fcis-2969	43	13	activation	activation	NOUN
fcis-2969	43	14	function	function	NOUN
fcis-2969	43	15	layer	layer	NOUN
fcis-2969	43	16	,	,	PUNCT
fcis-2969	43	17	and	and	CCONJ
fcis-2969	43	18	the	the	DET
fcis-2969	43	19	fifth	fifth	ADJ
fcis-2969	43	20	convolution	convolution	NOUN
fcis-2969	43	21	layer	layer	NOUN
fcis-2969	43	22	is	be	AUX
fcis-2969	43	23	followed	follow	VERB
fcis-2969	43	24	by	by	ADP
fcis-2969	43	25	a	a	DET
fcis-2969	43	26	pooling	pool	VERB
fcis-2969	43	27	layer	layer	NOUN
fcis-2969	43	28	.	.	PUNCT
fcis-2969	44	1	finally	finally	ADV
fcis-2969	44	2	,	,	PUNCT
fcis-2969	44	3	the	the	DET
fcis-2969	44	4	output	output	NOUN
fcis-2969	44	5	is	be	AUX
fcis-2969	44	6	delivered	deliver	VERB
fcis-2969	44	7	through	through	ADP
fcis-2969	44	8	three	three	NUM
fcis-2969	44	9	fully	fully	ADV
fcis-2969	44	10	connected	connect	VERB
fcis-2969	44	11	layers	layer	NOUN
fcis-2969	44	12	and	and	CCONJ
fcis-2969	44	13	the	the	DET
fcis-2969	44	14	relu	relu	NOUN
fcis-2969	44	15	activation	activation	NOUN
fcis-2969	44	16	function	function	NOUN
fcis-2969	44	17	layer	layer	NOUN
fcis-2969	44	18	and	and	CCONJ
fcis-2969	44	19	dropout	dropout	NOUN
fcis-2969	44	20	layer	layer	NOUN
fcis-2969	44	21	are	be	AUX
fcis-2969	44	22	added	add	VERB
fcis-2969	44	23	between	between	ADP
fcis-2969	44	24	the	the	DET
fcis-2969	44	25	fully	fully	ADV
fcis-2969	44	26	connected	connected	ADJ
fcis-2969	44	27	layers	layer	NOUN
fcis-2969	44	28	.	.	PUNCT
fcis-2969	45	1	this	this	DET
fcis-2969	45	2	network	network	NOUN
fcis-2969	45	3	can	can	AUX
fcis-2969	45	4	deal	deal	VERB
fcis-2969	45	5	with	with	ADP
fcis-2969	45	6	the	the	DET
fcis-2969	45	7	gradient	gradient	NOUN
fcis-2969	45	8	diffusion	diffusion	NOUN
fcis-2969	45	9	problem	problem	NOUN
fcis-2969	45	10	when	when	SCONJ
fcis-2969	45	11	the	the	DET
fcis-2969	45	12	network	network	NOUN
fcis-2969	45	13	is	be	AUX
fcis-2969	45	14	deep	deep	ADJ
fcis-2969	45	15	,	,	PUNCT
fcis-2969	45	16	and	and	CCONJ
fcis-2969	45	17	has	have	VERB
fcis-2969	45	18	good	good	ADJ
fcis-2969	45	19	anti	anti	ADJ
fcis-2969	45	20	-	-	ADJ
fcis-2969	45	21	overfitting	overfitting	ADJ
fcis-2969	45	22	ability	ability	NOUN
fcis-2969	45	23	and	and	CCONJ
fcis-2969	45	24	generalization	generalization	NOUN
fcis-2969	45	25	ability	ability	NOUN
fcis-2969	45	26	.	.	PUNCT
fcis-2969	46	1	the	the	DET
fcis-2969	46	2	structure	structure	NOUN
fcis-2969	46	3	is	be	AUX
fcis-2969	46	4	shown	show	VERB
fcis-2969	46	5	in	in	ADP
fcis-2969	46	6	figure	figure	NOUN
fcis-2969	46	7	2	2	NUM
fcis-2969	46	8	.	.	PUNCT
fcis-2969	46	9	figure	figure	NOUN
fcis-2969	46	10	2	2	NUM
fcis-2969	47	1	.	.	PUNCT
fcis-2969	47	2	alexnet	alexnet	ADJ
fcis-2969	47	3	structure	structure	NOUN
fcis-2969	47	4	drawing	draw	VERB
fcis-2969	47	5	the	the	DET
fcis-2969	47	6	key	key	NOUN
fcis-2969	47	7	to	to	ADP
fcis-2969	47	8	googlenet[5	googlenet[5	VERB
fcis-2969	47	9	]	]	PUNCT
fcis-2969	47	10	is	be	AUX
fcis-2969	47	11	the	the	DET
fcis-2969	47	12	inception	inception	NOUN
fcis-2969	47	13	module	module	NOUN
fcis-2969	47	14	.	.	PUNCT
fcis-2969	48	1	inception	inception	NOUN
fcis-2969	48	2	module	module	NOUN
fcis-2969	48	3	is	be	AUX
fcis-2969	48	4	divided	divide	VERB
fcis-2969	48	5	into	into	ADP
fcis-2969	48	6	four	four	NUM
fcis-2969	48	7	branches	branch	NOUN
fcis-2969	48	8	.	.	PUNCT
fcis-2969	49	1	by	by	ADP
fcis-2969	49	2	convolution	convolution	NOUN
fcis-2969	49	3	of	of	ADP
fcis-2969	49	4	multiple	multiple	ADJ
fcis-2969	49	5	scales	scale	NOUN
fcis-2969	49	6	at	at	ADP
fcis-2969	49	7	the	the	DET
fcis-2969	49	8	same	same	ADJ
fcis-2969	49	9	time	time	NOUN
fcis-2969	49	10	,	,	PUNCT
fcis-2969	49	11	richer	rich	ADJ
fcis-2969	49	12	features	feature	NOUN
fcis-2969	49	13	of	of	ADP
fcis-2969	49	14	different	different	ADJ
fcis-2969	49	15	scales	scale	NOUN
fcis-2969	49	16	can	can	AUX
fcis-2969	49	17	be	be	AUX
fcis-2969	49	18	extracted	extract	VERB
fcis-2969	49	19	,	,	PUNCT
fcis-2969	49	20	which	which	PRON
fcis-2969	49	21	makes	make	VERB
fcis-2969	49	22	the	the	DET
fcis-2969	49	23	final	final	ADJ
fcis-2969	49	24	recognition	recognition	NOUN
fcis-2969	49	25	effect	effect	NOUN
fcis-2969	49	26	of	of	ADP
fcis-2969	49	27	googlenet	googlenet	NOUN
fcis-2969	49	28	more	more	ADV
fcis-2969	49	29	accurate	accurate	ADJ
fcis-2969	49	30	.	.	PUNCT
fcis-2969	50	1	the	the	DET
fcis-2969	50	2	first	first	ADJ
fcis-2969	50	3	branch	branch	NOUN
fcis-2969	50	4	is	be	AUX
fcis-2969	50	5	a	a	DET
fcis-2969	50	6	1x1	1x1	NUM
fcis-2969	50	7	convolution	convolution	NOUN
fcis-2969	50	8	layer	layer	NOUN
fcis-2969	50	9	;	;	PUNCT
fcis-2969	50	10	the	the	DET
fcis-2969	50	11	second	second	ADJ
fcis-2969	50	12	branch	branch	NOUN
fcis-2969	50	13	goes	go	VERB
fcis-2969	50	14	first	first	ADV
fcis-2969	50	15	by	by	ADP
fcis-2969	50	16	a	a	DET
fcis-2969	50	17	convolution	convolution	NOUN
fcis-2969	50	18	of	of	ADP
fcis-2969	50	19	1x1	1x1	NUM
fcis-2969	50	20	and	and	CCONJ
fcis-2969	50	21	then	then	ADV
fcis-2969	50	22	by	by	ADP
fcis-2969	50	23	a	a	DET
fcis-2969	50	24	convolution	convolution	NOUN
fcis-2969	50	25	of	of	ADP
fcis-2969	50	26	3x3	3x3	NUM
fcis-2969	50	27	;	;	PUNCT
fcis-2969	50	28	the	the	DET
fcis-2969	50	29	third	third	ADJ
fcis-2969	50	30	branch	branch	NOUN
fcis-2969	50	31	is	be	AUX
fcis-2969	50	32	also	also	ADV
fcis-2969	50	33	first	first	ADV
fcis-2969	50	34	convolved	convolve	VERB
fcis-2969	50	35	by	by	ADP
fcis-2969	50	36	1x1	1x1	NUM
fcis-2969	50	37	and	and	CCONJ
fcis-2969	50	38	then	then	ADV
fcis-2969	50	39	by	by	ADP
fcis-2969	50	40	a	a	DET
fcis-2969	50	41	5x5	5x5	NUM
fcis-2969	50	42	convolution	convolution	NOUN
fcis-2969	50	43	;	;	PUNCT
fcis-2969	50	44	and	and	CCONJ
fcis-2969	50	45	the	the	DET
fcis-2969	50	46	fourth	fourth	ADJ
fcis-2969	50	47	branch	branch	NOUN
fcis-2969	50	48	is	be	AUX
fcis-2969	50	49	first	first	ADV
fcis-2969	50	50	maximized	maximize	VERB
fcis-2969	50	51	by	by	ADP
fcis-2969	50	52	3x3	3x3	NUM
fcis-2969	50	53	and	and	CCONJ
fcis-2969	50	54	then	then	ADV
fcis-2969	50	55	convolved	convolve	VERB
fcis-2969	50	56	by	by	ADP
fcis-2969	50	57	1x1	1x1	NUM
fcis-2969	50	58	.	.	PUNCT
fcis-2969	51	1	inception	inception	NOUN
fcis-2969	51	2	module	module	NOUN
fcis-2969	51	3	is	be	AUX
fcis-2969	51	4	to	to	PART
fcis-2969	51	5	achieve	achieve	VERB
fcis-2969	51	6	multi	multi	ADJ
fcis-2969	51	7	-	-	ADJ
fcis-2969	51	8	scale	scale	ADJ
fcis-2969	51	9	feature	feature	NOUN
fcis-2969	51	10	extraction	extraction	NOUN
fcis-2969	51	11	through	through	ADP
fcis-2969	51	12	the	the	DET
fcis-2969	51	13	convolution	convolution	NOUN
fcis-2969	51	14	and	and	CCONJ
fcis-2969	51	15	maximum	maximum	ADJ
fcis-2969	51	16	pooling	pooling	NOUN
fcis-2969	51	17	of	of	ADP
fcis-2969	51	18	these	these	DET
fcis-2969	51	19	three	three	NUM
fcis-2969	51	20	convolution	convolution	NOUN
fcis-2969	51	21	kernels	kernel	NOUN
fcis-2969	51	22	with	with	ADP
fcis-2969	51	23	different	different	ADJ
fcis-2969	51	24	sizes	size	NOUN
fcis-2969	51	25	,	,	PUNCT
fcis-2969	51	26	and	and	CCONJ
fcis-2969	51	27	the	the	DET
fcis-2969	51	28	addition	addition	NOUN
fcis-2969	51	29	of	of	ADP
fcis-2969	51	30	three	three	NUM
fcis-2969	51	31	1x1	1x1	NUM
fcis-2969	51	32	convolution	convolution	NOUN
fcis-2969	51	33	is	be	AUX
fcis-2969	51	34	because	because	SCONJ
fcis-2969	51	35	when	when	SCONJ
fcis-2969	51	36	the	the	DET
fcis-2969	51	37	sensitivity	sensitivity	NOUN
fcis-2969	51	38	field	field	NOUN
fcis-2969	51	39	is	be	AUX
fcis-2969	51	40	the	the	DET
fcis-2969	51	41	same	same	ADJ
fcis-2969	51	42	,	,	PUNCT
fcis-2969	51	43	more	more	ADV
fcis-2969	51	44	abundant	abundant	ADJ
fcis-2969	51	45	features	feature	NOUN
fcis-2969	51	46	can	can	AUX
fcis-2969	51	47	be	be	AUX
fcis-2969	51	48	extracted	extract	VERB
fcis-2969	51	49	by	by	ADP
fcis-2969	51	50	adding	add	VERB
fcis-2969	51	51	convolution	convolution	NOUN
fcis-2969	51	52	.	.	PUNCT
fcis-2969	52	1	googlenet	googlenet	PROPN
fcis-2969	52	2	also	also	ADV
fcis-2969	52	3	designed	design	VERB
fcis-2969	52	4	three	three	NUM
fcis-2969	52	5	loss	loss	NOUN
fcis-2969	52	6	units	unit	NOUN
fcis-2969	52	7	and	and	CCONJ
fcis-2969	52	8	replaced	replace	VERB
fcis-2969	52	9	the	the	DET
fcis-2969	52	10	full	full	ADJ
fcis-2969	52	11	connection	connection	NOUN
fcis-2969	52	12	layer	layer	NOUN
fcis-2969	52	13	in	in	ADP
fcis-2969	52	14	alexnet	alexnet	NOUN
fcis-2969	52	15	with	with	ADP
fcis-2969	52	16	global	global	ADJ
fcis-2969	52	17	averaging	averaging	NOUN
fcis-2969	52	18	pooling	pool	VERB
fcis-2969	52	19	to	to	PART
fcis-2969	52	20	reduce	reduce	VERB
fcis-2969	52	21	the	the	DET
fcis-2969	52	22	number	number	NOUN
fcis-2969	52	23	of	of	ADP
fcis-2969	52	24	parameters	parameter	NOUN
fcis-2969	52	25	.	.	PUNCT
fcis-2969	53	1	but	but	CCONJ
fcis-2969	53	2	the	the	DET
fcis-2969	53	3	network	network	NOUN
fcis-2969	53	4	as	as	ADP
fcis-2969	53	5	a	a	DET
fcis-2969	53	6	whole	whole	NOUN
fcis-2969	53	7	is	be	AUX
fcis-2969	53	8	complex	complex	ADJ
fcis-2969	53	9	.	.	PUNCT
fcis-2969	54	1	the	the	DET
fcis-2969	54	2	inception	inception	ADJ
fcis-2969	54	3	module	module	NOUN
fcis-2969	54	4	structure	structure	NOUN
fcis-2969	54	5	is	be	AUX
fcis-2969	54	6	shown	show	VERB
fcis-2969	54	7	in	in	ADP
fcis-2969	54	8	figure	figure	NOUN
fcis-2969	54	9	3	3	NUM
fcis-2969	54	10	.	.	PUNCT
fcis-2969	54	11	figure	figure	NOUN
fcis-2969	54	12	3	3	NUM
fcis-2969	54	13	.	.	PUNCT
fcis-2969	55	1	inception	inception	NOUN
fcis-2969	55	2	module	module	NOUN
fcis-2969	55	3	structure	structure	NOUN
fcis-2969	55	4	diagram	diagram	NOUN
fcis-2969	55	5	vggnet[6	vggnet[6	NOUN
fcis-2969	55	6	]	]	PUNCT
fcis-2969	55	7	has	have	VERB
fcis-2969	55	8	five	five	NUM
fcis-2969	55	9	sets	set	NOUN
fcis-2969	55	10	of	of	ADP
fcis-2969	55	11	convolution	convolution	NOUN
fcis-2969	55	12	kernels	kernel	NOUN
fcis-2969	55	13	with	with	ADP
fcis-2969	55	14	a	a	DET
fcis-2969	55	15	size	size	NOUN
fcis-2969	55	16	of	of	ADP
fcis-2969	55	17	3x3	3x3	NUM
fcis-2969	55	18	,	,	PUNCT
fcis-2969	55	19	two	two	NUM
fcis-2969	55	20	maximum	maximum	ADJ
fcis-2969	55	21	pooling	pool	VERB
fcis-2969	55	22	layers	layer	NOUN
fcis-2969	55	23	and	and	CCONJ
fcis-2969	55	24	three	three	NUM
fcis-2969	55	25	fully	fully	ADV
fcis-2969	55	26	connected	connected	ADJ
fcis-2969	55	27	layers	layer	NOUN
fcis-2969	55	28	.	.	PUNCT
fcis-2969	56	1	vggnet	vggnet	PROPN
fcis-2969	56	2	has	have	VERB
fcis-2969	56	3	6	6	NUM
fcis-2969	56	4	different	different	ADJ
fcis-2969	56	5	structures	structure	NOUN
fcis-2969	56	6	according	accord	VERB
fcis-2969	56	7	to	to	ADP
fcis-2969	56	8	different	different	ADJ
fcis-2969	56	9	network	network	NOUN
fcis-2969	56	10	setup	setup	NOUN
fcis-2969	56	11	methods	method	NOUN
fcis-2969	56	12	.	.	PUNCT
fcis-2969	57	1	vggnet	vggnet	PROPN
fcis-2969	57	2	has	have	VERB
fcis-2969	57	3	a	a	DET
fcis-2969	57	4	deeper	deep	ADJ
fcis-2969	57	5	network	network	NOUN
fcis-2969	57	6	structure	structure	NOUN
fcis-2969	57	7	,	,	PUNCT
fcis-2969	57	8	which	which	PRON
fcis-2969	57	9	deepens	deepen	VERB
fcis-2969	57	10	the	the	DET
fcis-2969	57	11	network	network	NOUN
fcis-2969	57	12	to	to	ADP
fcis-2969	57	13	19	19	NUM
fcis-2969	57	14	layers	layer	NOUN
fcis-2969	57	15	.	.	PUNCT
fcis-2969	58	1	the	the	DET
fcis-2969	58	2	previous	previous	ADJ
fcis-2969	58	3	convolutional	convolutional	ADJ
fcis-2969	58	4	layers	layer	NOUN
fcis-2969	58	5	are	be	AUX
fcis-2969	58	6	replaced	replace	VERB
fcis-2969	58	7	by	by	ADP
fcis-2969	58	8	convolutional	convolutional	ADJ
fcis-2969	58	9	blocks	block	NOUN
fcis-2969	58	10	composed	compose	VERB
fcis-2969	58	11	of	of	ADP
fcis-2969	58	12	different	different	ADJ
fcis-2969	58	13	numbers	number	NOUN
fcis-2969	58	14	of	of	ADP
fcis-2969	58	15	convolutional	convolutional	ADJ
fcis-2969	58	16	layers	layer	NOUN
fcis-2969	58	17	,	,	PUNCT
fcis-2969	58	18	which	which	PRON
fcis-2969	58	19	improves	improve	VERB
fcis-2969	58	20	the	the	DET
fcis-2969	58	21	receptive	receptive	ADJ
fcis-2969	58	22	field	field	NOUN
fcis-2969	58	23	of	of	ADP
fcis-2969	58	24	the	the	DET
fcis-2969	58	25	network	network	NOUN
fcis-2969	58	26	.	.	PUNCT
fcis-2969	59	1	the	the	DET
fcis-2969	59	2	key	key	NOUN
fcis-2969	59	3	to	to	ADP
fcis-2969	59	4	resnet[7	resnet[7	PRON
fcis-2969	59	5	]	]	X
fcis-2969	59	6	(	(	PUNCT
fcis-2969	59	7	residual	residual	ADJ
fcis-2969	59	8	neural	neural	ADJ
fcis-2969	59	9	network	network	NOUN
fcis-2969	59	10	)	)	PUNCT
fcis-2969	59	11	is	be	AUX
fcis-2969	59	12	the	the	DET
fcis-2969	59	13	residual	residual	ADJ
fcis-2969	59	14	block	block	NOUN
fcis-2969	59	15	.	.	PUNCT
fcis-2969	60	1	input	input	NOUN
fcis-2969	60	2	x	x	PRON
fcis-2969	60	3	passes	pass	VERB
fcis-2969	60	4	through	through	ADP
fcis-2969	60	5	two	two	NUM
fcis-2969	60	6	paths	path	NOUN
fcis-2969	60	7	respectively	respectively	ADV
fcis-2969	60	8	:	:	PUNCT
fcis-2969	60	9	one	one	NUM
fcis-2969	60	10	first	first	ADV
fcis-2969	60	11	passes	pass	VERB
fcis-2969	60	12	through	through	ADP
fcis-2969	60	13	3x3	3x3	NUM
fcis-2969	60	14	convolution	convolution	NOUN
fcis-2969	60	15	to	to	ADP
fcis-2969	60	16	a	a	DET
fcis-2969	60	17	bn	bn	NOUN
fcis-2969	60	18	layer	layer	NOUN
fcis-2969	60	19	and	and	CCONJ
fcis-2969	60	20	relu	relu	NOUN
fcis-2969	60	21	activation	activation	NOUN
fcis-2969	60	22	function	function	NOUN
fcis-2969	60	23	layer	layer	NOUN
fcis-2969	60	24	,	,	PUNCT
fcis-2969	60	25	and	and	CCONJ
fcis-2969	60	26	then	then	ADV
fcis-2969	60	27	passes	pass	VERB
fcis-2969	60	28	through	through	ADP
fcis-2969	60	29	a	a	DET
fcis-2969	60	30	3x3	3x3	NUM
fcis-2969	60	31	convolution	convolution	NOUN
fcis-2969	60	32	layer	layer	NOUN
fcis-2969	60	33	,	,	PUNCT
fcis-2969	60	34	bn	bn	NOUN
fcis-2969	60	35	layer	layer	NOUN
fcis-2969	60	36	and	and	CCONJ
fcis-2969	60	37	relu	relu	NOUN
fcis-2969	60	38	activation	activation	NOUN
fcis-2969	60	39	function	function	NOUN
fcis-2969	60	40	layer	layer	NOUN
fcis-2969	60	41	;	;	PUNCT
fcis-2969	60	42	the	the	DET
fcis-2969	60	43	other	other	ADJ
fcis-2969	60	44	way	way	NOUN
fcis-2969	60	45	is	be	AUX
fcis-2969	60	46	identity	identity	NOUN
fcis-2969	60	47	mapping	mapping	NOUN
fcis-2969	60	48	.	.	PUNCT
fcis-2969	61	1	the	the	DET
fcis-2969	61	2	results	result	NOUN
fcis-2969	61	3	of	of	ADP
fcis-2969	61	4	these	these	DET
fcis-2969	61	5	two	two	NUM
fcis-2969	61	6	branches	branch	NOUN
fcis-2969	61	7	are	be	AUX
fcis-2969	61	8	added	add	VERB
fcis-2969	61	9	together	together	ADV
fcis-2969	61	10	,	,	PUNCT
fcis-2969	61	11	and	and	CCONJ
fcis-2969	61	12	then	then	ADV
fcis-2969	61	13	a	a	DET
fcis-2969	61	14	relu	relu	NOUN
fcis-2969	61	15	activation	activation	NOUN
fcis-2969	61	16	function	function	NOUN
fcis-2969	61	17	layer	layer	NOUN
fcis-2969	61	18	is	be	AUX
fcis-2969	61	19	added	add	VERB
fcis-2969	61	20	to	to	AUX
fcis-2969	61	21	output	output	VERB
fcis-2969	61	22	.	.	PUNCT
fcis-2969	62	1	the	the	DET
fcis-2969	62	2	residual	residual	ADJ
fcis-2969	62	3	module	module	NOUN
fcis-2969	62	4	can	can	AUX
fcis-2969	62	5	solve	solve	VERB
fcis-2969	62	6	the	the	DET
fcis-2969	62	7	problem	problem	NOUN
fcis-2969	62	8	of	of	ADP
fcis-2969	62	9	gradient	gradient	ADJ
fcis-2969	62	10	explosion	explosion	NOUN
fcis-2969	62	11	and	and	CCONJ
fcis-2969	62	12	disappearance	disappearance	NOUN
fcis-2969	62	13	,	,	PUNCT
fcis-2969	62	14	and	and	CCONJ
fcis-2969	62	15	simplify	simplify	VERB
fcis-2969	62	16	the	the	DET
fcis-2969	62	17	learning	learning	NOUN
fcis-2969	62	18	objective	objective	NOUN
fcis-2969	62	19	and	and	CCONJ
fcis-2969	62	20	difficulty	difficulty	NOUN
fcis-2969	62	21	.	.	PUNCT
fcis-2969	63	1	through	through	ADP
fcis-2969	63	2	this	this	DET
fcis-2969	63	3	module	module	NOUN
fcis-2969	63	4	resnet	resnet	NOUN
fcis-2969	63	5	can	can	AUX
fcis-2969	63	6	realize	realize	VERB
fcis-2969	63	7	ultra	ultra	ADJ
fcis-2969	63	8	-	-	ADJ
fcis-2969	63	9	deep	deep	ADJ
fcis-2969	63	10	network	network	NOUN
fcis-2969	63	11	layer	layer	NOUN
fcis-2969	63	12	number	number	NOUN
fcis-2969	63	13	.	.	PUNCT
fcis-2969	64	1	resnet	resnet	NOUN
fcis-2969	64	2	keeps	keep	VERB
fcis-2969	64	3	stacking	stack	VERB
fcis-2969	64	4	this	this	DET
fcis-2969	64	5	basic	basic	ADJ
fcis-2969	64	6	module	module	NOUN
fcis-2969	64	7	to	to	PART
fcis-2969	64	8	get	get	VERB
fcis-2969	64	9	different	different	ADJ
fcis-2969	64	10	resnet	resnet	NOUN
fcis-2969	64	11	models	model	NOUN
fcis-2969	64	12	,	,	PUNCT
fcis-2969	64	13	common	common	ADJ
fcis-2969	64	14	resnet18	resnet18	NOUN
fcis-2969	64	15	,	,	PUNCT
fcis-2969	64	16	resnet50	resnet50	NOUN
fcis-2969	64	17	,	,	PUNCT
fcis-2969	64	18	resnet101	resnet101	PROPN
fcis-2969	64	19	and	and	CCONJ
fcis-2969	64	20	so	so	ADV
fcis-2969	64	21	on	on	ADV
fcis-2969	64	22	.	.	PUNCT
fcis-2969	65	1	the	the	DET
fcis-2969	65	2	residual	residual	ADJ
fcis-2969	65	3	structure	structure	NOUN
fcis-2969	65	4	is	be	AUX
fcis-2969	65	5	shown	show	VERB
fcis-2969	65	6	in	in	ADP
fcis-2969	65	7	figure	figure	NOUN
fcis-2969	65	8	4	4	NUM
fcis-2969	65	9	.	.	PUNCT
fcis-2969	65	10	figure	figure	VERB
fcis-2969	65	11	4	4	NUM
fcis-2969	65	12	.	.	PUNCT
fcis-2969	65	13	residual	residual	ADJ
fcis-2969	65	14	structure	structure	NOUN
fcis-2969	65	15	diagram	diagram	NOUN
fcis-2969	65	16	in	in	ADP
fcis-2969	65	17	addition	addition	NOUN
fcis-2969	65	18	to	to	ADP
fcis-2969	65	19	these	these	DET
fcis-2969	65	20	networks	network	NOUN
fcis-2969	65	21	,	,	PUNCT
fcis-2969	65	22	there	there	PRON
fcis-2969	65	23	are	be	VERB
fcis-2969	65	24	a	a	DET
fcis-2969	65	25	number	number	NOUN
fcis-2969	65	26	of	of	ADP
fcis-2969	65	27	pai	pai	PROPN
fcis-2969	65	28	sheng	sheng	PROPN
fcis-2969	65	29	frameworks	framework	NOUN
fcis-2969	65	30	based	base	VERB
fcis-2969	65	31	on	on	ADP
fcis-2969	65	32	them	they	PRON
fcis-2969	65	33	.	.	PUNCT
fcis-2969	66	1	a	a	DET
fcis-2969	66	2	localized	localize	VERB
fcis-2969	66	3	cnn[8	cnn[8	NOUN
fcis-2969	66	4	]	]	PUNCT
fcis-2969	66	5	is	be	AUX
fcis-2969	66	6	used	use	VERB
fcis-2969	66	7	to	to	PART
fcis-2969	66	8	extract	extract	VERB
fcis-2969	66	9	facial	facial	ADJ
fcis-2969	66	10	expression	expression	NOUN
fcis-2969	66	11	features	feature	NOUN
fcis-2969	66	12	.	.	PUNCT
fcis-2969	67	1	literature	literature	NOUN
fcis-2969	67	2	[	[	X
fcis-2969	67	3	9	9	NUM
fcis-2969	67	4	]	]	PUNCT
fcis-2969	67	5	proposed	propose	VERB
fcis-2969	67	6	a	a	DET
fcis-2969	67	7	facial	facial	ADJ
fcis-2969	67	8	expression	expression	NOUN
fcis-2969	67	9	recognition	recognition	NOUN
fcis-2969	67	10	method	method	NOUN
fcis-2969	67	11	based	base	VERB
fcis-2969	67	12	on	on	ADP
fcis-2969	67	13	region	region	NOUN
fcis-2969	67	14	of	of	ADP
fcis-2969	67	15	interest	interest	NOUN
fcis-2969	67	16	(	(	PUNCT
fcis-2969	67	17	roi	roi	NOUN
fcis-2969	67	18	)	)	PUNCT
fcis-2969	67	19	.	.	PUNCT
fcis-2969	68	1	wen	wen	PROPN
fcis-2969	68	2	xian	xian	PROPN
fcis-2969	69	1	[	[	X
fcis-2969	69	2	10	10	NUM
fcis-2969	69	3	]	]	PUNCT
fcis-2969	69	4	combines	combine	VERB
fcis-2969	69	5	the	the	DET
fcis-2969	69	6	area	area	NOUN
fcis-2969	69	7	of	of	ADP
fcis-2969	69	8	69	69	NUM
fcis-2969	69	9	interest	interest	NOUN
fcis-2969	69	10	and	and	CCONJ
fcis-2969	69	11	k	k	NOUN
fcis-2969	69	12	-	-	PUNCT
fcis-2969	69	13	nearest	near	ADJ
fcis-2969	69	14	neighbor	neighbor	NOUN
fcis-2969	69	15	(	(	PUNCT
fcis-2969	69	16	knn	knn	PROPN
fcis-2969	69	17	)	)	PUNCT
fcis-2969	69	18	algorithm	algorithm	NOUN
fcis-2969	69	19	to	to	PART
fcis-2969	69	20	propose	propose	VERB
fcis-2969	69	21	an	an	DET
fcis-2969	69	22	improved	improved	ADJ
fcis-2969	69	23	roi	roi	NOUN
fcis-2969	69	24	-	-	PUNCT
fcis-2969	69	25	knn	knn	PROPN
fcis-2969	69	26	training	training	NOUN
fcis-2969	69	27	method	method	NOUN
fcis-2969	69	28	,	,	PUNCT
fcis-2969	69	29	which	which	PRON
fcis-2969	69	30	solves	solve	VERB
fcis-2969	69	31	the	the	DET
fcis-2969	69	32	problem	problem	NOUN
fcis-2969	69	33	of	of	ADP
fcis-2969	69	34	poor	poor	ADJ
fcis-2969	69	35	generalization	generalization	NOUN
fcis-2969	69	36	ability	ability	NOUN
fcis-2969	69	37	of	of	ADP
fcis-2969	69	38	deep	deep	ADJ
fcis-2969	69	39	neural	neural	ADJ
fcis-2969	69	40	network	network	NOUN
fcis-2969	69	41	models	model	NOUN
fcis-2969	69	42	caused	cause	VERB
fcis-2969	69	43	by	by	ADP
fcis-2969	69	44	insufficient	insufficient	ADJ
fcis-2969	69	45	facial	facial	ADJ
fcis-2969	69	46	expression	expression	NOUN
fcis-2969	69	47	training	training	NOUN
fcis-2969	69	48	data	datum	NOUN
fcis-2969	69	49	,	,	PUNCT
fcis-2969	69	50	thus	thus	ADV
fcis-2969	69	51	improving	improve	VERB
fcis-2969	69	52	the	the	DET
fcis-2969	69	53	robustness	robustness	NOUN
fcis-2969	69	54	.	.	PUNCT
fcis-2969	70	1	2.4	2.4	NUM
fcis-2969	70	2	.	.	PUNCT
fcis-2969	71	1	generating	generate	VERB
fcis-2969	71	2	adversarial	adversarial	ADJ
fcis-2969	71	3	network	network	NOUN
fcis-2969	71	4	in	in	ADP
fcis-2969	71	5	2014	2014	NUM
fcis-2969	71	6	,	,	PUNCT
fcis-2969	71	7	ian	ian	PROPN
fcis-2969	71	8	goodfellow	goodfellow	PROPN
fcis-2969	71	9	proposed	propose	VERB
fcis-2969	71	10	a	a	DET
fcis-2969	71	11	generative	generative	ADJ
fcis-2969	71	12	adversarial	adversarial	ADJ
fcis-2969	71	13	networks	network	NOUN
fcis-2969	71	14	(	(	PUNCT
fcis-2969	71	15	gan	gan	PROPN
fcis-2969	71	16	)	)	PUNCT
fcis-2969	71	17	using	use	VERB
fcis-2969	71	18	an	an	DET
fcis-2969	71	19	unsupervised	unsupervised	ADJ
fcis-2969	71	20	architecture	architecture	NOUN
fcis-2969	71	21	.	.	PUNCT
fcis-2969	72	1	the	the	DET
fcis-2969	72	2	generative	generative	ADJ
fcis-2969	72	3	adversarial	adversarial	ADJ
fcis-2969	72	4	network	network	NOUN
fcis-2969	72	5	consists	consist	VERB
fcis-2969	72	6	of	of	ADP
fcis-2969	72	7	generator	generator	NOUN
fcis-2969	72	8	network	network	NOUN
fcis-2969	72	9	and	and	CCONJ
fcis-2969	72	10	discriminator	discriminator	NOUN
fcis-2969	72	11	network	network	NOUN
fcis-2969	72	12	,	,	PUNCT
fcis-2969	72	13	which	which	PRON
fcis-2969	72	14	can	can	AUX
fcis-2969	72	15	achieve	achieve	VERB
fcis-2969	72	16	better	well	ADJ
fcis-2969	72	17	output	output	NOUN
fcis-2969	72	18	by	by	ADP
fcis-2969	72	19	making	make	VERB
fcis-2969	72	20	them	they	PRON
fcis-2969	72	21	compete	compete	VERB
fcis-2969	72	22	with	with	ADP
fcis-2969	72	23	each	each	DET
fcis-2969	72	24	other	other	ADJ
fcis-2969	72	25	.	.	PUNCT
fcis-2969	73	1	in	in	ADP
fcis-2969	73	2	literature	literature	NOUN
fcis-2969	73	3	[	[	X
fcis-2969	73	4	11	11	NUM
fcis-2969	73	5	]	]	PUNCT
fcis-2969	73	6	,	,	PUNCT
fcis-2969	73	7	a	a	DET
fcis-2969	73	8	production	production	NOUN
fcis-2969	73	9	ad	ad	NOUN
fcis-2969	73	10	hoc	hoc	X
fcis-2969	73	11	network	network	NOUN
fcis-2969	73	12	(	(	PUNCT
fcis-2969	73	13	drgan	drgan	PROPN
fcis-2969	73	14	)	)	PUNCT
fcis-2969	73	15	for	for	ADP
fcis-2969	73	16	unwrapping	unwrap	VERB
fcis-2969	73	17	representation	representation	NOUN
fcis-2969	73	18	learning	learning	NOUN
fcis-2969	73	19	is	be	AUX
fcis-2969	73	20	proposed	propose	VERB
fcis-2969	73	21	.	.	PUNCT
fcis-2969	74	1	a	a	DET
fcis-2969	74	2	face	face	NOUN
fcis-2969	74	3	with	with	ADP
fcis-2969	74	4	arbitrary	arbitrary	ADJ
fcis-2969	74	5	posture	posture	NOUN
fcis-2969	74	6	or	or	CCONJ
fcis-2969	74	7	even	even	ADV
fcis-2969	74	8	extreme	extreme	ADJ
fcis-2969	74	9	profile	profile	NOUN
fcis-2969	74	10	can	can	AUX
fcis-2969	74	11	be	be	AUX
fcis-2969	74	12	positively	positively	ADV
fcis-2969	74	13	transformed	transform	VERB
fcis-2969	74	14	or	or	CCONJ
fcis-2969	74	15	rotated	rotate	VERB
fcis-2969	74	16	through	through	ADP
fcis-2969	74	17	a	a	DET
fcis-2969	74	18	codec	codec	NOUN
fcis-2969	74	19	structure	structure	NOUN
fcis-2969	74	20	generator	generator	NOUN
fcis-2969	74	21	,	,	PUNCT
fcis-2969	74	22	which	which	PRON
fcis-2969	74	23	has	have	VERB
fcis-2969	74	24	far	far	ADV
fcis-2969	74	25	-	-	PUNCT
fcis-2969	74	26	reaching	reach	VERB
fcis-2969	74	27	significance	significance	NOUN
fcis-2969	74	28	for	for	ADP
fcis-2969	74	29	the	the	DET
fcis-2969	74	30	study	study	NOUN
fcis-2969	74	31	of	of	ADP
fcis-2969	74	32	facial	facial	ADJ
fcis-2969	74	33	expression	expression	NOUN
fcis-2969	74	34	recognition	recognition	NOUN
fcis-2969	74	35	with	with	ADP
fcis-2969	74	36	low	low	ADJ
fcis-2969	74	37	robustness	robustness	NOUN
fcis-2969	74	38	in	in	ADP
fcis-2969	74	39	the	the	DET
fcis-2969	74	40	field	field	NOUN
fcis-2969	74	41	.	.	PUNCT
fcis-2969	75	1	3	3	X
fcis-2969	75	2	.	.	X
fcis-2969	75	3	conclusion	conclusion	VERB
fcis-2969	75	4	facial	facial	ADJ
fcis-2969	75	5	expression	expression	NOUN
fcis-2969	75	6	recognition	recognition	NOUN
fcis-2969	75	7	technology	technology	NOUN
fcis-2969	75	8	is	be	AUX
fcis-2969	75	9	becoming	become	VERB
fcis-2969	75	10	more	more	ADV
fcis-2969	75	11	and	and	CCONJ
fcis-2969	75	12	more	more	ADV
fcis-2969	75	13	mature	mature	ADJ
fcis-2969	75	14	,	,	PUNCT
fcis-2969	75	15	but	but	CCONJ
fcis-2969	75	16	there	there	PRON
fcis-2969	75	17	are	be	VERB
fcis-2969	75	18	still	still	ADV
fcis-2969	75	19	some	some	DET
fcis-2969	75	20	problems	problem	NOUN
fcis-2969	75	21	:	:	PUNCT
fcis-2969	75	22	(	(	PUNCT
fcis-2969	75	23	1	1	X
fcis-2969	75	24	)	)	PUNCT
fcis-2969	75	25	the	the	DET
fcis-2969	75	26	types	type	NOUN
fcis-2969	75	27	of	of	ADP
fcis-2969	75	28	facial	facial	ADJ
fcis-2969	75	29	expressions	expression	NOUN
fcis-2969	75	30	are	be	AUX
fcis-2969	75	31	not	not	PART
fcis-2969	75	32	rich	rich	ADJ
fcis-2969	75	33	enough	enough	ADV
fcis-2969	75	34	,	,	PUNCT
fcis-2969	75	35	human	human	ADJ
fcis-2969	75	36	expression	expression	NOUN
fcis-2969	75	37	is	be	AUX
fcis-2969	75	38	more	more	ADJ
fcis-2969	75	39	than	than	ADP
fcis-2969	75	40	six	six	NUM
fcis-2969	75	41	basic	basic	ADJ
fcis-2969	75	42	emotions	emotion	NOUN
fcis-2969	75	43	,	,	PUNCT
fcis-2969	75	44	which	which	PRON
fcis-2969	75	45	also	also	ADV
fcis-2969	75	46	leads	lead	VERB
fcis-2969	75	47	to	to	ADP
fcis-2969	75	48	the	the	DET
fcis-2969	75	49	poor	poor	ADJ
fcis-2969	75	50	recognition	recognition	NOUN
fcis-2969	75	51	effect	effect	NOUN
fcis-2969	75	52	of	of	ADP
fcis-2969	75	53	facial	facial	ADJ
fcis-2969	75	54	expression	expression	NOUN
fcis-2969	75	55	recognition	recognition	NOUN
fcis-2969	75	56	in	in	ADP
fcis-2969	75	57	natural	natural	ADJ
fcis-2969	75	58	and	and	CCONJ
fcis-2969	75	59	complex	complex	ADJ
fcis-2969	75	60	scenes	scene	NOUN
fcis-2969	75	61	.	.	PUNCT
fcis-2969	76	1	(	(	PUNCT
fcis-2969	76	2	2	2	X
fcis-2969	76	3	)	)	PUNCT
fcis-2969	76	4	lack	lack	NOUN
fcis-2969	76	5	of	of	ADP
fcis-2969	76	6	data	datum	NOUN
fcis-2969	76	7	sets	set	NOUN
fcis-2969	76	8	.	.	PUNCT
fcis-2969	77	1	most	most	ADJ
fcis-2969	77	2	of	of	ADP
fcis-2969	77	3	the	the	DET
fcis-2969	77	4	current	current	ADJ
fcis-2969	77	5	data	data	NOUN
fcis-2969	77	6	sets	set	NOUN
fcis-2969	77	7	are	be	AUX
fcis-2969	77	8	collected	collect	VERB
fcis-2969	77	9	in	in	ADP
fcis-2969	77	10	the	the	DET
fcis-2969	77	11	laboratory	laboratory	NOUN
fcis-2969	77	12	and	and	CCONJ
fcis-2969	77	13	other	other	ADJ
fcis-2969	77	14	scenes	scene	NOUN
fcis-2969	77	15	,	,	PUNCT
fcis-2969	77	16	and	and	CCONJ
fcis-2969	77	17	these	these	DET
fcis-2969	77	18	expressions	expression	NOUN
fcis-2969	77	19	are	be	AUX
fcis-2969	77	20	not	not	PART
fcis-2969	77	21	natural	natural	ADJ
fcis-2969	77	22	enough	enough	ADV
fcis-2969	77	23	.	.	PUNCT
fcis-2969	78	1	the	the	DET
fcis-2969	78	2	types	type	NOUN
fcis-2969	78	3	and	and	CCONJ
fcis-2969	78	4	quantity	quantity	NOUN
fcis-2969	78	5	of	of	ADP
fcis-2969	78	6	data	datum	NOUN
fcis-2969	78	7	sets	set	NOUN
fcis-2969	78	8	are	be	AUX
fcis-2969	78	9	not	not	PART
fcis-2969	78	10	enough	enough	ADJ
fcis-2969	78	11	,	,	PUNCT
fcis-2969	78	12	and	and	CCONJ
fcis-2969	78	13	deep	deep	ADJ
fcis-2969	78	14	learning	learning	NOUN
fcis-2969	78	15	requires	require	VERB
fcis-2969	78	16	a	a	DET
fcis-2969	78	17	lot	lot	NOUN
fcis-2969	78	18	of	of	ADP
fcis-2969	78	19	data	datum	NOUN
fcis-2969	78	20	for	for	ADP
fcis-2969	78	21	training	training	NOUN
fcis-2969	78	22	.	.	PUNCT
fcis-2969	79	1	rich	rich	ADJ
fcis-2969	79	2	data	datum	NOUN
fcis-2969	79	3	can	can	AUX
fcis-2969	79	4	make	make	VERB
fcis-2969	79	5	the	the	DET
fcis-2969	79	6	trained	train	VERB
fcis-2969	79	7	network	network	NOUN
fcis-2969	79	8	have	have	VERB
fcis-2969	79	9	better	well	ADJ
fcis-2969	79	10	performance	performance	NOUN
fcis-2969	79	11	.	.	PUNCT
fcis-2969	80	1	(	(	PUNCT
fcis-2969	80	2	3	3	X
fcis-2969	80	3	)	)	PUNCT
fcis-2969	80	4	although	although	SCONJ
fcis-2969	80	5	many	many	ADJ
fcis-2969	80	6	methods	method	NOUN
fcis-2969	80	7	based	base	VERB
fcis-2969	80	8	on	on	ADP
fcis-2969	80	9	deep	deep	ADJ
fcis-2969	80	10	learning	learning	NOUN
fcis-2969	80	11	have	have	VERB
fcis-2969	80	12	good	good	ADJ
fcis-2969	80	13	recognition	recognition	NOUN
fcis-2969	80	14	performance	performance	NOUN
fcis-2969	80	15	,	,	PUNCT
fcis-2969	80	16	their	their	PRON
fcis-2969	80	17	network	network	NOUN
fcis-2969	80	18	structure	structure	NOUN
fcis-2969	80	19	is	be	AUX
fcis-2969	80	20	too	too	ADV
fcis-2969	80	21	complex	complex	ADJ
fcis-2969	80	22	,	,	PUNCT
fcis-2969	80	23	requires	require	VERB
fcis-2969	80	24	high	high	ADJ
fcis-2969	80	25	hardware	hardware	NOUN
fcis-2969	80	26	requirements	requirement	NOUN
fcis-2969	80	27	,	,	PUNCT
fcis-2969	80	28	and	and	CCONJ
fcis-2969	80	29	requires	require	VERB
fcis-2969	80	30	too	too	ADV
fcis-2969	80	31	much	much	ADJ
fcis-2969	80	32	computation	computation	NOUN
fcis-2969	80	33	,	,	PUNCT
fcis-2969	80	34	which	which	PRON
fcis-2969	80	35	requires	require	VERB
fcis-2969	80	36	a	a	DET
fcis-2969	80	37	lot	lot	NOUN
fcis-2969	80	38	of	of	ADP
fcis-2969	80	39	training	training	NOUN
fcis-2969	80	40	time	time	NOUN
fcis-2969	80	41	.	.	PUNCT
fcis-2969	81	1	in	in	ADP
fcis-2969	81	2	the	the	DET
fcis-2969	81	3	future	future	ADJ
fcis-2969	81	4	research	research	NOUN
fcis-2969	81	5	,	,	PUNCT
fcis-2969	81	6	we	we	PRON
fcis-2969	81	7	should	should	AUX
fcis-2969	81	8	develop	develop	VERB
fcis-2969	81	9	more	more	ADJ
fcis-2969	81	10	new	new	ADJ
fcis-2969	81	11	algorithms	algorithm	NOUN
fcis-2969	81	12	with	with	ADP
fcis-2969	81	13	higher	high	ADJ
fcis-2969	81	14	recognition	recognition	NOUN
fcis-2969	81	15	effect	effect	NOUN
fcis-2969	81	16	and	and	CCONJ
fcis-2969	81	17	less	less	ADJ
fcis-2969	81	18	cost	cost	NOUN
fcis-2969	81	19	.	.	PUNCT
fcis-2969	82	1	the	the	DET
fcis-2969	82	2	network	network	NOUN
fcis-2969	82	3	is	be	AUX
fcis-2969	82	4	improved	improve	VERB
fcis-2969	82	5	by	by	ADP
fcis-2969	82	6	using	use	VERB
fcis-2969	82	7	lightweight	lightweight	ADJ
fcis-2969	82	8	model	model	NOUN
fcis-2969	82	9	,	,	PUNCT
fcis-2969	82	10	reducing	reduce	VERB
fcis-2969	82	11	network	network	NOUN
fcis-2969	82	12	parameters	parameter	NOUN
fcis-2969	82	13	and	and	CCONJ
fcis-2969	82	14	computation	computation	NOUN
fcis-2969	82	15	,	,	PUNCT
fcis-2969	82	16	greatly	greatly	ADV
fcis-2969	82	17	reducing	reduce	VERB
fcis-2969	82	18	the	the	DET
fcis-2969	82	19	training	training	NOUN
fcis-2969	82	20	time	time	NOUN
fcis-2969	82	21	,	,	PUNCT
fcis-2969	82	22	so	so	SCONJ
fcis-2969	82	23	that	that	SCONJ
fcis-2969	82	24	the	the	DET
fcis-2969	82	25	network	network	NOUN
fcis-2969	82	26	can	can	AUX
fcis-2969	82	27	be	be	AUX
fcis-2969	82	28	recognized	recognize	VERB
fcis-2969	82	29	in	in	ADP
fcis-2969	82	30	a	a	DET
fcis-2969	82	31	more	more	ADV
fcis-2969	82	32	complex	complex	ADJ
fcis-2969	82	33	environment	environment	NOUN
fcis-2969	82	34	.	.	PUNCT
fcis-2969	83	1	acknowledgment	acknowledgment	NOUN
fcis-2969	83	2	this	this	DET
fcis-2969	83	3	work	work	NOUN
fcis-2969	83	4	was	be	AUX
fcis-2969	83	5	supported	support	VERB
fcis-2969	83	6	in	in	ADP
fcis-2969	83	7	part	part	NOUN
fcis-2969	83	8	by	by	ADP
fcis-2969	83	9	the	the	DET
fcis-2969	83	10	2022	2022	NUM
fcis-2969	83	11	graduate	graduate	NOUN
fcis-2969	83	12	innovation	innovation	NOUN
fcis-2969	83	13	fund	fund	NOUN
fcis-2969	83	14	project	project	NOUN
fcis-2969	83	15	of	of	ADP
fcis-2969	83	16	sichuan	sichuan	PROPN
fcis-2969	83	17	university	university	PROPN
fcis-2969	83	18	of	of	ADP
fcis-2969	83	19	science	science	NOUN
fcis-2969	83	20	and	and	CCONJ
fcis-2969	83	21	engineering	engineering	NOUN
fcis-2969	83	22	(	(	PUNCT
fcis-2969	83	23	y2022146	y2022146	PROPN
fcis-2969	83	24	)	)	PUNCT
fcis-2969	83	25	.	.	PUNCT
fcis-2969	84	1	the	the	DET
fcis-2969	84	2	authors	author	NOUN
fcis-2969	84	3	express	express	VERB
fcis-2969	84	4	their	their	PRON
fcis-2969	84	5	acknowledgement	acknowledgement	NOUN
fcis-2969	84	6	for	for	ADP
fcis-2969	84	7	the	the	DET
fcis-2969	84	8	anonymous	anonymous	PROPN
fcis-2969	84	9	review	review	NOUN
fcis-2969	84	10	.	.	PUNCT
fcis-2969	85	1	references	reference	NOUN
fcis-2969	85	2	[	[	X
fcis-2969	85	3	1	1	NUM
fcis-2969	85	4	]	]	X
fcis-2969	85	5	hinton	hinton	PROPN
fcis-2969	85	6	ge	ge	PROPN
fcis-2969	85	7	,	,	PUNCT
fcis-2969	85	8	osinde	osinde	VERB
fcis-2969	85	9	ros	ros	PROPN
fcis-2969	85	10	,	,	PUNCT
fcis-2969	85	11	teh	teh	PROPN
fcis-2969	85	12	yw	yw	PROPN
fcis-2969	85	13	.	.	PUNCT
fcis-2969	86	1	a	a	DET
fcis-2969	86	2	fast	fast	ADJ
fcis-2969	86	3	learning	learn	VERB
fcis-2969	86	4	algorithm	algorithm	NOUN
fcis-2969	86	5	for	for	ADP
fcis-2969	86	6	deep	deep	ADJ
fcis-2969	86	7	belief	belief	NOUN
fcis-2969	86	8	nets[j	nets[j	ADP
fcis-2969	86	9	]	]	PUNCT
fcis-2969	86	10	.	.	PUNCT
fcis-2969	87	1	neural	neural	ADJ
fcis-2969	87	2	computation	computation	NOUN
fcis-2969	87	3	,	,	PUNCT
fcis-2969	87	4	2006	2006	NUM
fcis-2969	87	5	,	,	PUNCT
fcis-2969	87	6	18(7	18(7	NUM
fcis-2969	87	7	):	):	PUNCT
fcis-2969	87	8	1527	1527	NUM
fcis-2969	87	9	-	-	SYM
fcis-2969	87	10	1554	1554	NUM
fcis-2969	87	11	.	.	PUNCT
fcis-2969	88	1	[	[	X
fcis-2969	88	2	2	2	NUM
fcis-2969	88	3	]	]	X
fcis-2969	88	4	liu	liu	PROPN
fcis-2969	88	5	m	m	PROPN
fcis-2969	88	6	,	,	PUNCT
fcis-2969	88	7	li	li	PROPN
fcis-2969	88	8	s	s	PROPN
fcis-2969	88	9	,	,	PUNCT
fcis-2969	88	10	shan	shan	PROPN
fcis-2969	88	11	s	s	PROPN
fcis-2969	88	12	,	,	PUNCT
fcis-2969	88	13	et	et	PROPN
fcis-2969	88	14	al	al	PROPN
fcis-2969	88	15	.	.	PROPN
fcis-2969	88	16	au	au	PROPN
fcis-2969	88	17	-	-	PUNCT
fcis-2969	88	18	inspired	inspire	VERB
fcis-2969	88	19	deep	deep	ADJ
fcis-2969	88	20	networks	network	NOUN
fcis-2969	88	21	for	for	ADP
fcis-2969	88	22	facial	facial	ADJ
fcis-2969	88	23	expression	expression	NOUN
fcis-2969	88	24	feature	feature	NOUN
fcis-2969	88	25	learning	learn	VERB
fcis-2969	88	26	[	[	X
fcis-2969	88	27	j	j	X
fcis-2969	88	28	]	]	X
fcis-2969	88	29	.	.	PUNCT
fcis-2969	89	1	neurocomputing,2015	neurocomputing,2015	PROPN
fcis-2969	89	2	,	,	PUNCT
fcis-2969	89	3	159	159	NUM
fcis-2969	89	4	:	:	SYM
fcis-2969	89	5	126	126	NUM
fcis-2969	89	6	-	-	SYM
fcis-2969	89	7	136	136	NUM
fcis-2969	89	8	.	.	PUNCT
fcis-2969	90	1	[	[	X
fcis-2969	90	2	3	3	X
fcis-2969	90	3	]	]	X
fcis-2969	90	4	zeng	zeng	PROPN
fcis-2969	90	5	n	n	PROPN
fcis-2969	90	6	,	,	PUNCT
fcis-2969	90	7	zhang	zhang	PROPN
fcis-2969	90	8	h	h	PROPN
fcis-2969	90	9	,	,	PUNCT
fcis-2969	90	10	song	song	PROPN
fcis-2969	90	11	b	b	PROPN
fcis-2969	90	12	,	,	PUNCT
fcis-2969	90	13	et	et	PROPN
fcis-2969	90	14	al	al	PROPN
fcis-2969	90	15	.	.	PUNCT
fcis-2969	90	16	facial	facial	ADJ
fcis-2969	90	17	expression	expression	NOUN
fcis-2969	90	18	recognition	recognition	NOUN
fcis-2969	90	19	via	via	ADP
fcis-2969	90	20	learning	learn	VERB
fcis-2969	90	21	deep	deep	ADJ
fcis-2969	90	22	sparse	sparse	ADJ
fcis-2969	90	23	autoencoders	autoencoder	NOUN
fcis-2969	91	1	[	[	X
fcis-2969	91	2	j	j	X
fcis-2969	91	3	]	]	X
fcis-2969	91	4	.	.	PUNCT
fcis-2969	92	1	neurocomputing	neurocomputing	NOUN
fcis-2969	92	2	,	,	PUNCT
fcis-2969	92	3	2018	2018	NUM
fcis-2969	92	4	,	,	PUNCT
fcis-2969	92	5	273	273	NUM
fcis-2969	92	6	:	:	PUNCT
fcis-2969	92	7	643	643	NUM
fcis-2969	92	8	-	-	SYM
fcis-2969	92	9	649	649	NUM
fcis-2969	92	10	.	.	PUNCT
fcis-2969	93	1	[	[	X
fcis-2969	93	2	4	4	X
fcis-2969	93	3	]	]	PUNCT
fcis-2969	93	4	dhall	dhall	NOUN
fcis-2969	93	5	a	a	NOUN
fcis-2969	93	6	,	,	PUNCT
fcis-2969	93	7	goecke	goecke	NOUN
fcis-2969	93	8	r	r	PROPN
fcis-2969	93	9	,	,	PUNCT
fcis-2969	93	10	lucey	lucey	PROPN
fcis-2969	93	11	s	s	PROPN
fcis-2969	93	12	,	,	PUNCT
fcis-2969	93	13	et	et	PROPN
fcis-2969	93	14	al	al	PROPN
fcis-2969	93	15	.	.	PUNCT
fcis-2969	94	1	collecting	collect	VERB
fcis-2969	94	2	large	large	ADJ
fcis-2969	94	3	,	,	PUNCT
fcis-2969	94	4	richly	richly	ADV
fcis-2969	94	5	annotated	annotate	VERB
fcis-2969	94	6	facial	facial	ADJ
fcis-2969	94	7	-	-	PUNCT
fcis-2969	94	8	expression	expression	NOUN
fcis-2969	94	9	databases	database	NOUN
fcis-2969	94	10	from	from	ADP
fcis-2969	94	11	movies[j	movies[j	PROPN
fcis-2969	94	12	]	]	PUNCT
fcis-2969	94	13	.	.	PUNCT
fcis-2969	95	1	ieee	ieee	NOUN
fcis-2969	95	2	multimedia	multimedia	NOUN
fcis-2969	95	3	,	,	PUNCT
fcis-2969	95	4	2012	2012	NUM
fcis-2969	95	5	,	,	PUNCT
fcis-2969	95	6	19(3	19(3	NUM
fcis-2969	95	7	):	):	PUNCT
fcis-2969	95	8	34	34	NUM
fcis-2969	95	9	-	-	SYM
fcis-2969	95	10	41	41	NUM
fcis-2969	95	11	.	.	PUNCT
fcis-2969	96	1	[	[	X
fcis-2969	96	2	5	5	X
fcis-2969	96	3	]	]	PUNCT
fcis-2969	96	4	he	he	PRON
fcis-2969	96	5	k	k	PROPN
fcis-2969	96	6	,	,	PUNCT
fcis-2969	96	7	zhang	zhang	PROPN
fcis-2969	96	8	x	x	PROPN
fcis-2969	96	9	,	,	PUNCT
fcis-2969	96	10	ren	ren	PROPN
fcis-2969	96	11	s	s	PROPN
fcis-2969	96	12	,	,	PUNCT
fcis-2969	96	13	et	et	PROPN
fcis-2969	96	14	al	al	PROPN
fcis-2969	96	15	.	.	PUNCT
fcis-2969	97	1	deep	deep	ADJ
fcis-2969	97	2	residual	residual	ADJ
fcis-2969	97	3	learning	learning	NOUN
fcis-2969	97	4	for	for	ADP
fcis-2969	97	5	image	image	NOUN
fcis-2969	97	6	recognition[c	recognition[c	PROPN
fcis-2969	97	7	]	]	PUNCT
fcis-2969	97	8	.	.	PUNCT
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fcis-2969	98	4	ieee	ieee	NOUN
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fcis-2969	98	8	vision	vision	NOUN
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fcis-2969	98	11	recognition	recognition	NOUN
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fcis-2969	98	14	:	:	PUNCT
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fcis-2969	98	16	-	-	SYM
fcis-2969	98	17	778	778	NUM
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fcis-2969	99	3	]	]	SYM
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fcis-2969	99	6	,	,	PUNCT
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fcis-2969	99	8	a.	a.	NOUN
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fcis-2969	99	13	for	for	ADP
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fcis-2969	99	17	image	image	NOUN
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fcis-2969	99	19	]	]	PUNCT
fcis-2969	99	20	.	.	PUNCT
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fcis-2969	100	5	,	,	PUNCT
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fcis-2969	100	7	):	):	PUNCT
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fcis-2969	100	9	-	-	SYM
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fcis-2969	100	11	.	.	PUNCT
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fcis-2969	101	9	,	,	PUNCT
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fcis-2969	101	16	networks[c	networks[c	PROPN
fcis-2969	101	17	]	]	PUNCT
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fcis-2969	101	30	-	-	NUM
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fcis-2969	101	32	.	.	PUNCT
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fcis-2969	102	2	8	8	NUM
fcis-2969	102	3	]	]	X
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fcis-2969	102	6	,	,	PUNCT
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fcis-2969	102	9	,	,	PUNCT
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fcis-2969	102	14	al	al	PROPN
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fcis-2969	102	33	computer	computer	NOUN
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fcis-2969	102	42	-	-	SYM
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fcis-2969	103	3	]	]	SYM
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fcis-2969	103	20	cnns	cnn	NOUN
fcis-2969	103	21	[	[	X
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fcis-2969	103	24	on	on	ADP
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fcis-2969	103	27	.	.	PUNCT
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fcis-2969	104	2	,	,	PUNCT
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fcis-2969	104	4	,	,	PUNCT
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fcis-2969	104	6	:	:	PUNCT
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fcis-2969	104	8	-	-	SYM
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fcis-2969	104	10	.	.	PUNCT
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fcis-2969	105	3	]	]	X
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fcis-2969	105	5	s	s	PROPN
fcis-2969	105	6	,	,	PUNCT
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fcis-2969	105	9	,	,	PUNCT
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fcis-2969	105	11	ren	ren	PROPN
fcis-2969	105	12	,	,	PUNCT
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fcis-2969	105	19	-	-	PUNCT
fcis-2969	105	20	knn	knn	VERB
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fcis-2969	105	22	neural	neural	ADJ
fcis-2969	105	23	network	network	NOUN
fcis-2969	105	24	[	[	X
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fcis-2969	105	33	):	):	PUNCT
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fcis-2969	105	35	-	-	SYM
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fcis-2969	106	3	]	]	PUNCT
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fcis-2969	106	6	,	,	PUNCT
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fcis-2969	106	9	,	,	PUNCT
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fcis-2969	106	11	x.	x.	PROPN
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fcis-2969	106	14	learning	learn	VERB
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fcis-2969	106	17	pose	pose	NOUN
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fcis-2969	106	24	the	the	DET
fcis-2969	106	25	ieee	ieee	NOUN
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fcis-2969	106	27	on	on	ADP
fcis-2969	106	28	computer	computer	NOUN
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fcis-2969	106	30	and	and	CCONJ
fcis-2969	106	31	pattern	pattern	NOUN
fcis-2969	106	32	recognition	recognition	NOUN
fcis-2969	106	33	,	,	PUNCT
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fcis-2969	106	35	:	:	SYM
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