id	sid	tid	token	lemma	pos
brj-23849	1	1	peer	peer	NOUN
brj-23849	1	2	-	-	PUNCT
brj-23849	1	3	review	review	NOUN
brj-23849	1	4	article	article	NOUN
brj-23849	1	5	peerreviewed	peerreviewe	VERB
brj-23849	1	6	article	article	NOUN
brj-23849	1	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	1	8	teng	teng	PROPN
brj-23849	1	9	et	et	PROPN
brj-23849	1	10	al	al	PROPN
brj-23849	1	11	.	.	PROPN
brj-23849	2	1	(	(	PUNCT
brj-23849	2	2	2024	2024	NUM
brj-23849	2	3	)	)	PUNCT
brj-23849	2	4	.	.	PUNCT
brj-23849	3	1	“	"	PUNCT
brj-23849	3	2	dcgan	dcgan	VERB
brj-23849	3	3	enhancement	enhancement	NOUN
brj-23849	3	4	method	method	NOUN
brj-23849	3	5	,	,	PUNCT
brj-23849	3	6	”	"	PUNCT
brj-23849	3	7	bioresources	bioresource	NOUN
brj-23849	3	8	19(4	19(4	NUM
brj-23849	3	9	)	)	PUNCT
brj-23849	3	10	,	,	PUNCT
brj-23849	3	11	9271	9271	NUM
brj-23849	3	12	-	-	SYM
brj-23849	3	13	9284	9284	NUM
brj-23849	3	14	.	.	PUNCT
brj-23849	4	1	9271	9271	NUM
brj-23849	4	2	an	an	DET
brj-23849	4	3	improved	improved	ADJ
brj-23849	4	4	dcgan	dcgan	NOUN
brj-23849	4	5	-	-	PUNCT
brj-23849	4	6	based	base	VERB
brj-23849	4	7	recognition	recognition	NOUN
brj-23849	4	8	enhancement	enhancement	NOUN
brj-23849	4	9	method	method	NOUN
brj-23849	4	10	for	for	ADP
brj-23849	4	11	american	american	PROPN
brj-23849	4	12	hyphantria	hyphantria	PROPN
brj-23849	4	13	cunea	cunea	PROPN
brj-23849	4	14	larvae	larvae	PROPN
brj-23849	4	15	net	net	ADJ
brj-23849	4	16	curtain	curtain	NOUN
brj-23849	4	17	image	image	NOUN
brj-23849	4	18	dataset	dataset	VERB
brj-23849	4	19	shaomin	shaomin	NOUN
brj-23849	4	20	teng	teng	PROPN
brj-23849	4	21	,	,	PUNCT
brj-23849	4	22	a	a	PRON
brj-23849	4	23	,	,	PUNCT
brj-23849	4	24	b	b	NOUN
brj-23849	4	25	chengming	chengme	VERB
brj-23849	4	26	wang	wang	PROPN
brj-23849	4	27	,	,	PUNCT
brj-23849	4	28	c	c	PROPN
brj-23849	4	29	shunyi	shunyi	PROPN
brj-23849	4	30	shang	shang	PROPN
brj-23849	4	31	,	,	PUNCT
brj-23849	4	32	c	c	PROPN
brj-23849	4	33	yuxuan	yuxuan	PROPN
brj-23849	4	34	tuo	tuo	PROPN
brj-23849	4	35	,	,	PUNCT
brj-23849	4	36	c	c	PROPN
brj-23849	4	37	and	and	CCONJ
brj-23849	4	38	decheng	decheng	PROPN
brj-23849	4	39	wang	wang	PROPN
brj-23849	4	40	a	a	PROPN
brj-23849	4	41	,	,	PUNCT
brj-23849	4	42	*	*	PUNCT
brj-23849	4	43	the	the	DET
brj-23849	4	44	fall	fall	NOUN
brj-23849	4	45	webworm	webworm	NOUN
brj-23849	4	46	(	(	PUNCT
brj-23849	4	47	hyphantria	hyphantria	X
brj-23849	4	48	cunea	cunea	PROPN
brj-23849	4	49	)	)	PUNCT
brj-23849	4	50	poses	pose	VERB
brj-23849	4	51	a	a	DET
brj-23849	4	52	significant	significant	ADJ
brj-23849	4	53	threat	threat	NOUN
brj-23849	4	54	to	to	ADP
brj-23849	4	55	agriculture	agriculture	NOUN
brj-23849	4	56	,	,	PUNCT
brj-23849	4	57	as	as	SCONJ
brj-23849	4	58	its	its	PRON
brj-23849	4	59	larvae	larvae	NOUN
brj-23849	4	60	feed	feed	NOUN
brj-23849	4	61	on	on	ADP
brj-23849	4	62	leaves	leave	NOUN
brj-23849	4	63	and	and	CCONJ
brj-23849	4	64	form	form	VERB
brj-23849	4	65	silken	silken	ADJ
brj-23849	4	66	webs	webs	NOUN
brj-23849	4	67	,	,	PUNCT
brj-23849	4	68	which	which	PRON
brj-23849	4	69	can	can	AUX
brj-23849	4	70	severely	severely	ADV
brj-23849	4	71	impact	impact	VERB
brj-23849	4	72	plant	plant	NOUN
brj-23849	4	73	growth	growth	NOUN
brj-23849	4	74	.	.	PUNCT
brj-23849	5	1	however	however	ADV
brj-23849	5	2	,	,	PUNCT
brj-23849	5	3	the	the	DET
brj-23849	5	4	lack	lack	NOUN
brj-23849	5	5	of	of	ADP
brj-23849	5	6	specific	specific	ADJ
brj-23849	5	7	image	image	NOUN
brj-23849	5	8	datasets	dataset	NOUN
brj-23849	5	9	for	for	ADP
brj-23849	5	10	the	the	DET
brj-23849	5	11	larvae	larvae	NOUN
brj-23849	5	12	’s	’s	PART
brj-23849	5	13	webs	webs	NOUN
brj-23849	5	14	hinders	hinder	VERB
brj-23849	5	15	the	the	DET
brj-23849	5	16	use	use	NOUN
brj-23849	5	17	of	of	ADP
brj-23849	5	18	image	image	NOUN
brj-23849	5	19	recognition	recognition	NOUN
brj-23849	5	20	technologies	technology	NOUN
brj-23849	5	21	in	in	ADP
brj-23849	5	22	pest	pest	NOUN
brj-23849	5	23	prevention	prevention	NOUN
brj-23849	5	24	and	and	CCONJ
brj-23849	5	25	control	control	NOUN
brj-23849	5	26	.	.	PUNCT
brj-23849	6	1	to	to	PART
brj-23849	6	2	address	address	VERB
brj-23849	6	3	this	this	DET
brj-23849	6	4	issue	issue	NOUN
brj-23849	6	5	,	,	PUNCT
brj-23849	6	6	an	an	DET
brj-23849	6	7	enhancement	enhancement	NOUN
brj-23849	6	8	method	method	NOUN
brj-23849	6	9	is	be	AUX
brj-23849	6	10	proposed	propose	VERB
brj-23849	6	11	here	here	ADV
brj-23849	6	12	based	base	VERB
brj-23849	6	13	on	on	ADP
brj-23849	6	14	an	an	DET
brj-23849	6	15	improved	improve	VERB
brj-23849	6	16	deep	deep	ADJ
brj-23849	6	17	convolutional	convolutional	ADJ
brj-23849	6	18	generative	generative	ADJ
brj-23849	6	19	adversarial	adversarial	ADJ
brj-23849	6	20	network	network	NOUN
brj-23849	6	21	(	(	PUNCT
brj-23849	6	22	dcgan	dcgan	NOUN
brj-23849	6	23	)	)	PUNCT
brj-23849	6	24	.	.	PUNCT
brj-23849	7	1	this	this	DET
brj-23849	7	2	method	method	NOUN
brj-23849	7	3	generates	generate	VERB
brj-23849	7	4	a	a	DET
brj-23849	7	5	diverse	diverse	ADJ
brj-23849	7	6	set	set	NOUN
brj-23849	7	7	of	of	ADP
brj-23849	7	8	high	high	ADJ
brj-23849	7	9	-	-	PUNCT
brj-23849	7	10	quality	quality	NOUN
brj-23849	7	11	web	web	NOUN
brj-23849	7	12	images	image	NOUN
brj-23849	7	13	,	,	PUNCT
brj-23849	7	14	significantly	significantly	ADV
brj-23849	7	15	expanding	expand	VERB
brj-23849	7	16	the	the	DET
brj-23849	7	17	existing	exist	VERB
brj-23849	7	18	dataset	dataset	NOUN
brj-23849	7	19	.	.	PUNCT
brj-23849	8	1	experimental	experimental	ADJ
brj-23849	8	2	results	result	NOUN
brj-23849	8	3	demonstrated	demonstrate	VERB
brj-23849	8	4	that	that	SCONJ
brj-23849	8	5	this	this	DET
brj-23849	8	6	enhanced	enhance	VERB
brj-23849	8	7	dataset	dataset	NOUN
brj-23849	8	8	improved	improve	VERB
brj-23849	8	9	the	the	DET
brj-23849	8	10	robustness	robustness	NOUN
brj-23849	8	11	of	of	ADP
brj-23849	8	12	recognition	recognition	NOUN
brj-23849	8	13	networks	network	NOUN
brj-23849	8	14	,	,	PUNCT
brj-23849	8	15	enabling	enable	VERB
brj-23849	8	16	better	well	ADJ
brj-23849	8	17	automatic	automatic	ADJ
brj-23849	8	18	identification	identification	NOUN
brj-23849	8	19	and	and	CCONJ
brj-23849	8	20	precision	precision	NOUN
brj-23849	8	21	spraying	spray	VERB
brj-23849	8	22	to	to	PART
brj-23849	8	23	control	control	VERB
brj-23849	8	24	hyphantria	hyphantria	PROPN
brj-23849	8	25	cunea	cunea	PROPN
brj-23849	8	26	.	.	PUNCT
brj-23849	9	1	this	this	DET
brj-23849	9	2	approach	approach	NOUN
brj-23849	9	3	not	not	PART
brj-23849	9	4	only	only	ADV
brj-23849	9	5	advances	advance	VERB
brj-23849	9	6	automated	automate	VERB
brj-23849	9	7	pest	pest	NOUN
brj-23849	9	8	monitoring	monitoring	NOUN
brj-23849	9	9	in	in	ADP
brj-23849	9	10	agriculture	agriculture	NOUN
brj-23849	9	11	but	but	CCONJ
brj-23849	9	12	also	also	ADV
brj-23849	9	13	offers	offer	VERB
brj-23849	9	14	new	new	ADJ
brj-23849	9	15	possibilities	possibility	NOUN
brj-23849	9	16	for	for	ADP
brj-23849	9	17	applying	apply	VERB
brj-23849	9	18	similar	similar	ADJ
brj-23849	9	19	technologies	technology	NOUN
brj-23849	9	20	to	to	ADP
brj-23849	9	21	the	the	DET
brj-23849	9	22	identification	identification	NOUN
brj-23849	9	23	of	of	ADP
brj-23849	9	24	other	other	ADJ
brj-23849	9	25	plant	plant	NOUN
brj-23849	9	26	pests	pest	NOUN
brj-23849	9	27	.	.	PUNCT
brj-23849	10	1	doi	doi	NOUN
brj-23849	10	2	:	:	PUNCT
brj-23849	10	3	10.15376	10.15376	NUM
brj-23849	10	4	/	/	SYM
brj-23849	10	5	biores.19.4.9271	biores.19.4.9271	PROPN
brj-23849	10	6	-	-	PUNCT
brj-23849	10	7	9284	9284	NUM
brj-23849	10	8	keywords	keyword	NOUN
brj-23849	10	9	:	:	PUNCT
brj-23849	10	10	american	american	PROPN
brj-23849	10	11	hyphantria	hyphantria	PROPN
brj-23849	10	12	cunea	cunea	PROPN
brj-23849	10	13	larvae	larvae	PROPN
brj-23849	10	14	net	net	ADJ
brj-23849	10	15	curtain	curtain	NOUN
brj-23849	10	16	;	;	PUNCT
brj-23849	10	17	generative	generative	ADJ
brj-23849	10	18	adversarial	adversarial	ADJ
brj-23849	10	19	network	network	NOUN
brj-23849	10	20	;	;	PUNCT
brj-23849	10	21	data	datum	NOUN
brj-23849	10	22	enhancement	enhancement	NOUN
brj-23849	10	23	;	;	PUNCT
brj-23849	10	24	convolutional	convolutional	ADJ
brj-23849	10	25	neural	neural	ADJ
brj-23849	10	26	network	network	NOUN
brj-23849	10	27	;	;	PUNCT
brj-23849	10	28	checkerboard	checkerboard	NOUN
brj-23849	10	29	effect	effect	NOUN
brj-23849	10	30	;	;	PUNCT
brj-23849	10	31	plant	plant	NOUN
brj-23849	10	32	pest	pest	NOUN
brj-23849	10	33	control	control	NOUN
brj-23849	10	34	;	;	PUNCT
brj-23849	10	35	patent	patent	NOUN
brj-23849	10	36	contact	contact	NOUN
brj-23849	10	37	information	information	NOUN
brj-23849	10	38	:	:	PUNCT
brj-23849	10	39	a	a	X
brj-23849	10	40	:	:	PUNCT
brj-23849	10	41	college	college	NOUN
brj-23849	10	42	of	of	ADP
brj-23849	10	43	engineering	engineering	PROPN
brj-23849	10	44	,	,	PUNCT
brj-23849	10	45	china	china	PROPN
brj-23849	10	46	agricultural	agricultural	PROPN
brj-23849	10	47	university	university	PROPN
brj-23849	10	48	,	,	PUNCT
brj-23849	10	49	beijing	beijing	PROPN
brj-23849	10	50	100083	100083	NUM
brj-23849	10	51	,	,	PUNCT
brj-23849	10	52	china	china	PROPN
brj-23849	10	53	;	;	PUNCT
brj-23849	10	54	b	b	X
brj-23849	10	55	:	:	PUNCT
brj-23849	10	56	menoble	menoble	PROPN
brj-23849	10	57	co.	co.	PROPN
brj-23849	10	58	,	,	PUNCT
brj-23849	10	59	ltd	ltd	PROPN
brj-23849	10	60	.	.	PROPN
brj-23849	10	61	,	,	PUNCT
brj-23849	10	62	beijing	beijing	PROPN
brj-23849	10	63	100083	100083	NUM
brj-23849	10	64	,	,	PUNCT
brj-23849	10	65	china	china	PROPN
brj-23849	10	66	;	;	PUNCT
brj-23849	10	67	c	c	X
brj-23849	10	68	:	:	PUNCT
brj-23849	10	69	school	school	NOUN
brj-23849	10	70	of	of	ADP
brj-23849	10	71	mechanical	mechanical	ADJ
brj-23849	10	72	and	and	CCONJ
brj-23849	10	73	automotive	automotive	ADJ
brj-23849	10	74	engineering	engineering	NOUN
brj-23849	10	75	,	,	PUNCT
brj-23849	10	76	liaocheng	liaocheng	PROPN
brj-23849	10	77	university	university	PROPN
brj-23849	10	78	,	,	PUNCT
brj-23849	10	79	liaocheng	liaocheng	PROPN
brj-23849	10	80	252059	252059	NUM
brj-23849	10	81	,	,	PUNCT
brj-23849	10	82	china	china	PROPN
brj-23849	10	83	;	;	PUNCT
brj-23849	10	84	*	*	PUNCT
brj-23849	10	85	corresponding	correspond	VERB
brj-23849	10	86	author	author	NOUN
brj-23849	10	87	:	:	PUNCT
brj-23849	10	88	wdc@cau.edu.cn	wdc@cau.edu.cn	ADJ
brj-23849	10	89	introduction	introduction	NOUN
brj-23849	10	90	the	the	DET
brj-23849	10	91	american	american	PROPN
brj-23849	10	92	hyphantria	hyphantria	PROPN
brj-23849	10	93	cunea	cunea	PROPN
brj-23849	10	94	,	,	PUNCT
brj-23849	10	95	also	also	ADV
brj-23849	10	96	known	know	VERB
brj-23849	10	97	as	as	ADP
brj-23849	10	98	the	the	DET
brj-23849	10	99	autumn	autumn	NOUN
brj-23849	10	100	curtain	curtain	NOUN
brj-23849	10	101	moth	moth	NOUN
brj-23849	10	102	,	,	PUNCT
brj-23849	10	103	is	be	AUX
brj-23849	10	104	a	a	DET
brj-23849	10	105	key	key	ADJ
brj-23849	10	106	target	target	NOUN
brj-23849	10	107	for	for	ADP
brj-23849	10	108	forestry	forestry	NOUN
brj-23849	10	109	control	control	NOUN
brj-23849	10	110	because	because	SCONJ
brj-23849	10	111	of	of	ADP
brj-23849	10	112	its	its	PRON
brj-23849	10	113	high	high	ADJ
brj-23849	10	114	reproduction	reproduction	NOUN
brj-23849	10	115	rate	rate	NOUN
brj-23849	10	116	and	and	CCONJ
brj-23849	10	117	rapid	rapid	ADJ
brj-23849	10	118	spread	spread	NOUN
brj-23849	10	119	(	(	PUNCT
brj-23849	10	120	yang	yang	PROPN
brj-23849	10	121	et	et	PROPN
brj-23849	10	122	al	al	PROPN
brj-23849	10	123	.	.	PROPN
brj-23849	10	124	2008	2008	NUM
brj-23849	10	125	)	)	PUNCT
brj-23849	10	126	.	.	PUNCT
brj-23849	11	1	the	the	DET
brj-23849	11	2	damage	damage	NOUN
brj-23849	11	3	of	of	ADP
brj-23849	11	4	american	american	PROPN
brj-23849	11	5	h.	h.	PROPN
brj-23849	11	6	cunea	cunea	PROPN
brj-23849	11	7	is	be	AUX
brj-23849	11	8	primarily	primarily	ADV
brj-23849	11	9	caused	cause	VERB
brj-23849	11	10	by	by	ADP
brj-23849	11	11	larvae	larvae	NOUN
brj-23849	11	12	feeding	feed	VERB
brj-23849	11	13	on	on	ADP
brj-23849	11	14	leaves	leave	NOUN
brj-23849	11	15	,	,	PUNCT
brj-23849	11	16	and	and	CCONJ
brj-23849	11	17	the	the	DET
brj-23849	11	18	larvae	larvae	NOUN
brj-23849	11	19	start	start	VERB
brj-23849	11	20	to	to	PART
brj-23849	11	21	feed	feed	VERB
brj-23849	11	22	a	a	DET
brj-23849	11	23	few	few	ADJ
brj-23849	11	24	hours	hour	NOUN
brj-23849	11	25	after	after	ADP
brj-23849	11	26	hatching	hatch	VERB
brj-23849	11	27	and	and	CCONJ
brj-23849	11	28	spit	spit	VERB
brj-23849	11	29	out	out	ADP
brj-23849	11	30	silk	silk	NOUN
brj-23849	11	31	and	and	CCONJ
brj-23849	11	32	form	form	VERB
brj-23849	11	33	a	a	DET
brj-23849	11	34	net	net	ADJ
brj-23849	11	35	curtain	curtain	NOUN
brj-23849	11	36	.	.	PUNCT
brj-23849	12	1	the	the	DET
brj-23849	12	2	whole	whole	ADJ
brj-23849	12	3	larval	larval	ADJ
brj-23849	12	4	stage	stage	NOUN
brj-23849	12	5	feeds	feed	VERB
brj-23849	12	6	heavily	heavily	ADV
brj-23849	12	7	,	,	PUNCT
brj-23849	12	8	causing	cause	VERB
brj-23849	12	9	low	low	ADJ
brj-23849	12	10	resilience	resilience	NOUN
brj-23849	12	11	of	of	ADP
brj-23849	12	12	the	the	DET
brj-23849	12	13	tree	tree	NOUN
brj-23849	12	14	and	and	CCONJ
brj-23849	12	15	even	even	ADV
brj-23849	12	16	death	death	NOUN
brj-23849	12	17	of	of	ADP
brj-23849	12	18	the	the	DET
brj-23849	12	19	whole	whole	ADJ
brj-23849	12	20	plant	plant	NOUN
brj-23849	12	21	in	in	ADP
brj-23849	12	22	severe	severe	ADJ
brj-23849	12	23	cases	case	NOUN
brj-23849	12	24	(	(	PUNCT
brj-23849	12	25	haijun	haijun	PROPN
brj-23849	12	26	et	et	PROPN
brj-23849	12	27	al	al	PROPN
brj-23849	12	28	.	.	PROPN
brj-23849	12	29	2006	2006	NUM
brj-23849	12	30	)	)	PUNCT
brj-23849	12	31	.	.	PUNCT
brj-23849	13	1	the	the	DET
brj-23849	13	2	larval	larval	ADJ
brj-23849	13	3	stage	stage	NOUN
brj-23849	13	4	has	have	VERB
brj-23849	13	5	a	a	DET
brj-23849	13	6	clear	clear	ADJ
brj-23849	13	7	net	net	ADJ
brj-23849	13	8	curtain	curtain	NOUN
brj-23849	13	9	,	,	PUNCT
brj-23849	13	10	which	which	PRON
brj-23849	13	11	becomes	become	VERB
brj-23849	13	12	the	the	DET
brj-23849	13	13	best	good	ADJ
brj-23849	13	14	period	period	NOUN
brj-23849	13	15	for	for	ADP
brj-23849	13	16	control	control	NOUN
brj-23849	13	17	.	.	PUNCT
brj-23849	14	1	chemical	chemical	NOUN
brj-23849	14	2	control	control	NOUN
brj-23849	14	3	is	be	AUX
brj-23849	14	4	currently	currently	ADV
brj-23849	14	5	the	the	DET
brj-23849	14	6	most	most	ADV
brj-23849	14	7	effective	effective	ADJ
brj-23849	14	8	method	method	NOUN
brj-23849	14	9	.	.	PUNCT
brj-23849	15	1	the	the	DET
brj-23849	15	2	common	common	ADJ
brj-23849	15	3	way	way	NOUN
brj-23849	15	4	is	be	AUX
brj-23849	15	5	to	to	PART
brj-23849	15	6	manually	manually	ADV
brj-23849	15	7	spray	spray	VERB
brj-23849	15	8	the	the	DET
brj-23849	15	9	chemical	chemical	ADJ
brj-23849	15	10	agent	agent	NOUN
brj-23849	15	11	on	on	ADP
brj-23849	15	12	a	a	DET
brj-23849	15	13	large	large	ADJ
brj-23849	15	14	scale	scale	NOUN
brj-23849	15	15	.	.	PUNCT
brj-23849	16	1	however	however	ADV
brj-23849	16	2	,	,	PUNCT
brj-23849	16	3	this	this	DET
brj-23849	16	4	method	method	NOUN
brj-23849	16	5	is	be	AUX
brj-23849	16	6	inefficient	inefficient	ADJ
brj-23849	16	7	and	and	CCONJ
brj-23849	16	8	causes	cause	VERB
brj-23849	16	9	serious	serious	ADJ
brj-23849	16	10	environmental	environmental	ADJ
brj-23849	16	11	pollution	pollution	NOUN
brj-23849	16	12	.	.	PUNCT
brj-23849	17	1	there	there	PRON
brj-23849	17	2	is	be	VERB
brj-23849	17	3	an	an	DET
brj-23849	17	4	urgent	urgent	ADJ
brj-23849	17	5	need	need	NOUN
brj-23849	17	6	for	for	SCONJ
brj-23849	17	7	the	the	DET
brj-23849	17	8	emergence	emergence	NOUN
brj-23849	17	9	of	of	ADP
brj-23849	17	10	an	an	DET
brj-23849	17	11	intelligent	intelligent	ADJ
brj-23849	17	12	spraying	spraying	NOUN
brj-23849	17	13	technology	technology	NOUN
brj-23849	17	14	to	to	PART
brj-23849	17	15	achieve	achieve	VERB
brj-23849	17	16	accurate	accurate	ADJ
brj-23849	17	17	automated	automate	VERB
brj-23849	17	18	on	on	ADP
brj-23849	17	19	target	target	NOUN
brj-23849	17	20	spraying	spray	VERB
brj-23849	17	21	operations	operation	NOUN
brj-23849	17	22	.	.	PUNCT
brj-23849	18	1	accurate	accurate	ADJ
brj-23849	18	2	target	target	NOUN
brj-23849	18	3	identification	identification	NOUN
brj-23849	18	4	is	be	AUX
brj-23849	18	5	a	a	DET
brj-23849	18	6	prerequisite	prerequisite	NOUN
brj-23849	18	7	for	for	ADP
brj-23849	18	8	achieving	achieve	VERB
brj-23849	18	9	on	on	ADP
brj-23849	18	10	-	-	PUNCT
brj-23849	18	11	target	target	NOUN
brj-23849	18	12	spraying	spraying	NOUN
brj-23849	18	13	.	.	PUNCT
brj-23849	19	1	in	in	ADP
brj-23849	19	2	recent	recent	ADJ
brj-23849	19	3	years	year	NOUN
brj-23849	19	4	,	,	PUNCT
brj-23849	19	5	with	with	ADP
brj-23849	19	6	the	the	DET
brj-23849	19	7	development	development	NOUN
brj-23849	19	8	of	of	ADP
brj-23849	19	9	neural	neural	ADJ
brj-23849	19	10	networks	network	NOUN
brj-23849	19	11	,	,	PUNCT
brj-23849	19	12	many	many	ADJ
brj-23849	19	13	deep	deep	ADJ
brj-23849	19	14	learning	learning	NOUN
brj-23849	19	15	-	-	PUNCT
brj-23849	19	16	based	base	VERB
brj-23849	19	17	methods	method	NOUN
brj-23849	19	18	have	have	AUX
brj-23849	19	19	been	be	AUX
brj-23849	19	20	widely	widely	ADV
brj-23849	19	21	promoted	promote	VERB
brj-23849	19	22	and	and	CCONJ
brj-23849	19	23	applied	apply	VERB
brj-23849	19	24	in	in	ADP
brj-23849	19	25	the	the	DET
brj-23849	19	26	field	field	NOUN
brj-23849	19	27	of	of	ADP
brj-23849	19	28	pest	pest	NOUN
brj-23849	19	29	and	and	CCONJ
brj-23849	19	30	disease	disease	NOUN
brj-23849	19	31	identification	identification	NOUN
brj-23849	19	32	in	in	ADP
brj-23849	19	33	agriculture	agriculture	NOUN
brj-23849	19	34	and	and	CCONJ
brj-23849	19	35	forestry	forestry	NOUN
brj-23849	19	36	,	,	PUNCT
brj-23849	19	37	while	while	SCONJ
brj-23849	19	38	deep	deep	ADJ
brj-23849	19	39	learning	learning	NOUN
brj-23849	19	40	algorithms	algorithm	NOUN
brj-23849	19	41	require	require	VERB
brj-23849	19	42	huge	huge	ADJ
brj-23849	19	43	data	datum	NOUN
brj-23849	19	44	sets	set	NOUN
brj-23849	19	45	as	as	ADP
brj-23849	19	46	training	training	NOUN
brj-23849	19	47	support	support	NOUN
brj-23849	19	48	(	(	PUNCT
brj-23849	19	49	ding	ding	NOUN
brj-23849	19	50	et	et	PROPN
brj-23849	19	51	al	al	PROPN
brj-23849	19	52	.	.	PROPN
brj-23849	19	53	2019	2019	NUM
brj-23849	19	54	)	)	PUNCT
brj-23849	19	55	.	.	PUNCT
brj-23849	20	1	for	for	ADP
brj-23849	20	2	the	the	DET
brj-23849	20	3	acquiring	acquiring	NOUN
brj-23849	20	4	of	of	ADP
brj-23849	20	5	images	image	NOUN
brj-23849	20	6	of	of	ADP
brj-23849	20	7	american	american	PROPN
brj-23849	20	8	h.	h.	PROPN
brj-23849	20	9	cunea	cunea	PROPN
brj-23849	20	10	larvae	larvae	PROPN
brj-23849	20	11	net	net	ADJ
brj-23849	20	12	curtains	curtain	NOUN
brj-23849	20	13	,	,	PUNCT
brj-23849	20	14	there	there	PRON
brj-23849	20	15	are	be	VERB
brj-23849	20	16	problems	problem	NOUN
brj-23849	20	17	,	,	PUNCT
brj-23849	20	18	such	such	ADJ
brj-23849	20	19	as	as	ADP
brj-23849	20	20	peerreviewed	peerreviewe	VERB
brj-23849	20	21	article	article	NOUN
brj-23849	20	22	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	20	23	teng	teng	PROPN
brj-23849	20	24	et	et	PROPN
brj-23849	20	25	al	al	PROPN
brj-23849	20	26	.	.	PROPN
brj-23849	21	1	(	(	PUNCT
brj-23849	21	2	2024	2024	NUM
brj-23849	21	3	)	)	PUNCT
brj-23849	21	4	.	.	PUNCT
brj-23849	22	1	“	"	PUNCT
brj-23849	22	2	dcgan	dcgan	VERB
brj-23849	22	3	enhancement	enhancement	NOUN
brj-23849	22	4	method	method	NOUN
brj-23849	22	5	,	,	PUNCT
brj-23849	22	6	”	"	PUNCT
brj-23849	22	7	bioresources	bioresource	NOUN
brj-23849	22	8	19(4	19(4	NUM
brj-23849	22	9	)	)	PUNCT
brj-23849	22	10	,	,	PUNCT
brj-23849	22	11	9271	9271	NUM
brj-23849	22	12	-	-	SYM
brj-23849	22	13	9284	9284	NUM
brj-23849	22	14	.	.	PUNCT
brj-23849	23	1	9272	9272	NUM
brj-23849	24	1	the	the	DET
brj-23849	24	2	difficulty	difficulty	NOUN
brj-23849	24	3	of	of	ADP
brj-23849	24	4	acquiring	acquire	VERB
brj-23849	24	5	images	image	NOUN
brj-23849	24	6	of	of	ADP
brj-23849	24	7	net	net	ADJ
brj-23849	24	8	curtains	curtain	NOUN
brj-23849	24	9	in	in	ADP
brj-23849	24	10	higher	high	ADJ
brj-23849	24	11	and	and	CCONJ
brj-23849	24	12	deeper	deep	ADJ
brj-23849	24	13	parts	part	NOUN
brj-23849	24	14	of	of	ADP
brj-23849	24	15	the	the	DET
brj-23849	24	16	bush	bush	PROPN
brj-23849	24	17	,	,	PUNCT
brj-23849	24	18	high	high	ADJ
brj-23849	24	19	manual	manual	ADJ
brj-23849	24	20	acquisition	acquisition	NOUN
brj-23849	24	21	workload	workload	NOUN
brj-23849	24	22	,	,	PUNCT
brj-23849	24	23	and	and	CCONJ
brj-23849	24	24	large	large	ADJ
brj-23849	24	25	differences	difference	NOUN
brj-23849	24	26	in	in	ADP
brj-23849	24	27	images	image	NOUN
brj-23849	24	28	under	under	ADP
brj-23849	24	29	different	different	ADJ
brj-23849	24	30	lighting	lighting	NOUN
brj-23849	24	31	conditions	condition	NOUN
brj-23849	24	32	,	,	PUNCT
brj-23849	24	33	which	which	PRON
brj-23849	24	34	make	make	VERB
brj-23849	24	35	it	it	PRON
brj-23849	24	36	difficult	difficult	ADJ
brj-23849	24	37	to	to	PART
brj-23849	24	38	form	form	VERB
brj-23849	24	39	a	a	DET
brj-23849	24	40	sufficiently	sufficiently	ADV
brj-23849	24	41	large	large	ADJ
brj-23849	24	42	database	database	NOUN
brj-23849	24	43	.	.	PUNCT
brj-23849	25	1	to	to	PART
brj-23849	25	2	expand	expand	VERB
brj-23849	25	3	the	the	DET
brj-23849	25	4	original	original	ADJ
brj-23849	25	5	dataset	dataset	NOUN
brj-23849	25	6	and	and	CCONJ
brj-23849	25	7	enhance	enhance	VERB
brj-23849	25	8	the	the	DET
brj-23849	25	9	generalization	generalization	NOUN
brj-23849	25	10	ability	ability	NOUN
brj-23849	25	11	of	of	ADP
brj-23849	25	12	neural	neural	ADJ
brj-23849	25	13	network	network	NOUN
brj-23849	25	14	models	model	NOUN
brj-23849	25	15	(	(	PUNCT
brj-23849	25	16	yang	yang	PROPN
brj-23849	25	17	and	and	CCONJ
brj-23849	25	18	li	li	PROPN
brj-23849	25	19	2021	2021	NUM
brj-23849	25	20	)	)	PUNCT
brj-23849	25	21	,	,	PUNCT
brj-23849	25	22	many	many	ADJ
brj-23849	25	23	methods	method	NOUN
brj-23849	25	24	have	have	AUX
brj-23849	25	25	been	be	AUX
brj-23849	25	26	proposed	propose	VERB
brj-23849	25	27	.	.	PUNCT
brj-23849	26	1	some	some	DET
brj-23849	26	2	literature	literature	NOUN
brj-23849	26	3	presented	present	VERB
brj-23849	26	4	enhanced	enhance	VERB
brj-23849	26	5	datasets	dataset	NOUN
brj-23849	26	6	using	use	VERB
brj-23849	26	7	geometric	geometric	ADJ
brj-23849	26	8	transformations	transformation	NOUN
brj-23849	26	9	of	of	ADP
brj-23849	26	10	the	the	DET
brj-23849	26	11	original	original	ADJ
brj-23849	26	12	images	image	NOUN
brj-23849	26	13	(	(	PUNCT
brj-23849	26	14	including	include	VERB
brj-23849	26	15	various	various	ADJ
brj-23849	26	16	operations	operation	NOUN
brj-23849	26	17	such	such	ADJ
brj-23849	26	18	as	as	ADP
brj-23849	26	19	deformation	deformation	NOUN
brj-23849	26	20	,	,	PUNCT
brj-23849	26	21	cropping	cropping	NOUN
brj-23849	26	22	,	,	PUNCT
brj-23849	26	23	mirroring	mirroring	NOUN
brj-23849	26	24	,	,	PUNCT
brj-23849	26	25	scaling	scaling	NOUN
brj-23849	26	26	,	,	PUNCT
brj-23849	26	27	and	and	CCONJ
brj-23849	26	28	rotation	rotation	NOUN
brj-23849	26	29	)	)	PUNCT
brj-23849	26	30	(	(	PUNCT
brj-23849	26	31	de	de	X
brj-23849	26	32	andrade	andrade	PROPN
brj-23849	26	33	2019	2019	NUM
brj-23849	26	34	)	)	PUNCT
brj-23849	26	35	;	;	PUNCT
brj-23849	26	36	some	some	DET
brj-23849	26	37	literature	literature	NOUN
brj-23849	26	38	adjusted	adjust	VERB
brj-23849	26	39	the	the	DET
brj-23849	26	40	brightness	brightness	NOUN
brj-23849	26	41	and	and	CCONJ
brj-23849	26	42	contrast	contrast	NOUN
brj-23849	26	43	of	of	ADP
brj-23849	26	44	the	the	DET
brj-23849	26	45	original	original	ADJ
brj-23849	26	46	images	image	NOUN
brj-23849	26	47	randomly	randomly	ADV
brj-23849	26	48	or	or	CCONJ
brj-23849	26	49	added	add	VERB
brj-23849	26	50	random	random	ADJ
brj-23849	26	51	noise	noise	NOUN
brj-23849	26	52	to	to	ADP
brj-23849	26	53	the	the	DET
brj-23849	26	54	original	original	ADJ
brj-23849	26	55	images	image	NOUN
brj-23849	26	56	,	,	PUNCT
brj-23849	26	57	increasing	increase	VERB
brj-23849	26	58	the	the	DET
brj-23849	26	59	number	number	NOUN
brj-23849	26	60	of	of	ADP
brj-23849	26	61	samples	sample	NOUN
brj-23849	26	62	in	in	ADP
brj-23849	26	63	the	the	DET
brj-23849	26	64	dataset	dataset	NOUN
brj-23849	26	65	(	(	PUNCT
brj-23849	26	66	lopes	lope	NOUN
brj-23849	26	67	et	et	PROPN
brj-23849	26	68	al	al	PROPN
brj-23849	26	69	.	.	PROPN
brj-23849	26	70	2017	2017	NUM
brj-23849	26	71	)	)	PUNCT
brj-23849	26	72	,	,	PUNCT
brj-23849	26	73	using	use	VERB
brj-23849	26	74	the	the	DET
brj-23849	26	75	method	method	NOUN
brj-23849	26	76	of	of	ADP
brj-23849	26	77	adding	add	VERB
brj-23849	26	78	gaussian	gaussian	ADJ
brj-23849	26	79	noise	noise	NOUN
brj-23849	26	80	to	to	ADP
brj-23849	26	81	the	the	DET
brj-23849	26	82	images	image	NOUN
brj-23849	26	83	to	to	PART
brj-23849	26	84	generate	generate	VERB
brj-23849	26	85	new	new	ADJ
brj-23849	26	86	images	image	NOUN
brj-23849	26	87	;	;	PUNCT
brj-23849	26	88	some	some	DET
brj-23849	26	89	other	other	ADJ
brj-23849	26	90	literature	literature	NOUN
brj-23849	26	91	adopted	adopt	VERB
brj-23849	26	92	the	the	DET
brj-23849	26	93	method	method	NOUN
brj-23849	26	94	of	of	ADP
brj-23849	26	95	randomly	randomly	ADV
brj-23849	26	96	intercepting	intercept	VERB
brj-23849	26	97	or	or	CCONJ
brj-23849	26	98	randomly	randomly	ADV
brj-23849	26	99	masking	mask	VERB
brj-23849	26	100	a	a	DET
brj-23849	26	101	part	part	NOUN
brj-23849	26	102	of	of	ADP
brj-23849	26	103	the	the	DET
brj-23849	26	104	images	image	NOUN
brj-23849	26	105	(	(	PUNCT
brj-23849	26	106	sun	sun	NOUN
brj-23849	26	107	et	et	PROPN
brj-23849	26	108	al	al	PROPN
brj-23849	26	109	.	.	PROPN
brj-23849	26	110	2017	2017	NUM
brj-23849	26	111	)	)	PUNCT
brj-23849	26	112	,	,	PUNCT
brj-23849	26	113	using	use	VERB
brj-23849	26	114	the	the	DET
brj-23849	26	115	replacement	replacement	NOUN
brj-23849	26	116	of	of	ADP
brj-23849	26	117	different	different	ADJ
brj-23849	26	118	regions	region	NOUN
brj-23849	26	119	of	of	ADP
brj-23849	26	120	the	the	DET
brj-23849	26	121	images	image	NOUN
brj-23849	26	122	to	to	PART
brj-23849	26	123	generate	generate	VERB
brj-23849	26	124	new	new	ADJ
brj-23849	26	125	images	image	NOUN
brj-23849	26	126	.	.	PUNCT
brj-23849	27	1	these	these	DET
brj-23849	27	2	methods	method	NOUN
brj-23849	27	3	do	do	AUX
brj-23849	27	4	not	not	PART
brj-23849	27	5	take	take	VERB
brj-23849	27	6	full	full	ADJ
brj-23849	27	7	advantage	advantage	NOUN
brj-23849	27	8	of	of	ADP
brj-23849	27	9	the	the	DET
brj-23849	27	10	intrinsic	intrinsic	ADJ
brj-23849	27	11	characteristics	characteristic	NOUN
brj-23849	27	12	of	of	ADP
brj-23849	27	13	the	the	DET
brj-23849	27	14	original	original	ADJ
brj-23849	27	15	samples	sample	NOUN
brj-23849	27	16	,	,	PUNCT
brj-23849	27	17	resulting	result	VERB
brj-23849	27	18	in	in	ADP
brj-23849	27	19	a	a	DET
brj-23849	27	20	trained	train	VERB
brj-23849	27	21	neural	neural	ADJ
brj-23849	27	22	network	network	NOUN
brj-23849	27	23	model	model	NOUN
brj-23849	27	24	with	with	ADP
brj-23849	27	25	limitations	limitation	NOUN
brj-23849	27	26	and	and	CCONJ
brj-23849	27	27	poor	poor	ADJ
brj-23849	27	28	generalization	generalization	NOUN
brj-23849	27	29	ability	ability	NOUN
brj-23849	27	30	.	.	PUNCT
brj-23849	28	1	to	to	PART
brj-23849	28	2	solve	solve	VERB
brj-23849	28	3	this	this	DET
brj-23849	28	4	problem	problem	NOUN
brj-23849	28	5	,	,	PUNCT
brj-23849	28	6	automatic	automatic	ADJ
brj-23849	28	7	image	image	NOUN
brj-23849	28	8	generation	generation	NOUN
brj-23849	28	9	was	be	AUX
brj-23849	28	10	created	create	VERB
brj-23849	28	11	and	and	CCONJ
brj-23849	28	12	in	in	ADP
brj-23849	28	13	2004	2004	NUM
brj-23849	28	14	,	,	PUNCT
brj-23849	28	15	a	a	DET
brj-23849	28	16	method	method	NOUN
brj-23849	28	17	to	to	PART
brj-23849	28	18	generate	generate	VERB
brj-23849	28	19	new	new	ADJ
brj-23849	28	20	datasets	dataset	NOUN
brj-23849	28	21	using	use	VERB
brj-23849	28	22	neural	neural	ADJ
brj-23849	28	23	networks	network	NOUN
brj-23849	28	24	was	be	AUX
brj-23849	28	25	first	first	ADV
brj-23849	28	26	proposed	propose	VERB
brj-23849	28	27	in	in	ADP
brj-23849	28	28	the	the	DET
brj-23849	28	29	literature	literature	NOUN
brj-23849	28	30	(	(	PUNCT
brj-23849	28	31	zhou	zhou	PROPN
brj-23849	28	32	and	and	CCONJ
brj-23849	28	33	jiang	jiang	PROPN
brj-23849	28	34	2004	2004	NUM
brj-23849	28	35	)	)	PUNCT
brj-23849	28	36	.	.	PUNCT
brj-23849	29	1	since	since	SCONJ
brj-23849	29	2	then	then	ADV
brj-23849	29	3	,	,	PUNCT
brj-23849	29	4	it	it	PRON
brj-23849	29	5	has	have	AUX
brj-23849	29	6	been	be	AUX
brj-23849	29	7	one	one	NUM
brj-23849	29	8	of	of	ADP
brj-23849	29	9	the	the	DET
brj-23849	29	10	key	key	ADJ
brj-23849	29	11	research	research	NOUN
brj-23849	29	12	directions	direction	NOUN
brj-23849	29	13	in	in	ADP
brj-23849	29	14	the	the	DET
brj-23849	29	15	field	field	NOUN
brj-23849	29	16	of	of	ADP
brj-23849	29	17	machine	machine	NOUN
brj-23849	29	18	vision	vision	NOUN
brj-23849	29	19	(	(	PUNCT
brj-23849	29	20	radford	radford	PROPN
brj-23849	29	21	et	et	PROPN
brj-23849	29	22	al	al	PROPN
brj-23849	29	23	.	.	PROPN
brj-23849	29	24	2015	2015	NUM
brj-23849	29	25	;	;	PUNCT
brj-23849	29	26	isola	isola	PROPN
brj-23849	29	27	et	et	PROPN
brj-23849	29	28	al	al	PROPN
brj-23849	29	29	.	.	PROPN
brj-23849	29	30	2016	2016	NUM
brj-23849	29	31	;	;	PUNCT
brj-23849	29	32	grant	grant	NOUN
brj-23849	29	33	-	-	PUNCT
brj-23849	29	34	jacob	jacob	PROPN
brj-23849	29	35	et	et	PROPN
brj-23849	29	36	al	al	PROPN
brj-23849	29	37	.	.	PROPN
brj-23849	29	38	2022	2022	NUM
brj-23849	29	39	)	)	PUNCT
brj-23849	29	40	.	.	PUNCT
brj-23849	30	1	meanwhile	meanwhile	ADV
brj-23849	30	2	,	,	PUNCT
brj-23849	30	3	the	the	DET
brj-23849	30	4	rapid	rapid	ADJ
brj-23849	30	5	development	development	NOUN
brj-23849	30	6	of	of	ADP
brj-23849	30	7	deep	deep	ADJ
brj-23849	30	8	learning	learning	NOUN
brj-23849	30	9	has	have	AUX
brj-23849	30	10	greatly	greatly	ADV
brj-23849	30	11	facilitated	facilitate	VERB
brj-23849	30	12	the	the	DET
brj-23849	30	13	development	development	NOUN
brj-23849	30	14	of	of	ADP
brj-23849	30	15	image	image	NOUN
brj-23849	30	16	generation	generation	NOUN
brj-23849	30	17	techniques	technique	NOUN
brj-23849	30	18	.	.	PUNCT
brj-23849	31	1	the	the	DET
brj-23849	31	2	proposal	proposal	NOUN
brj-23849	31	3	of	of	ADP
brj-23849	31	4	generative	generative	ADJ
brj-23849	31	5	adversarial	adversarial	ADJ
brj-23849	31	6	networks	network	NOUN
brj-23849	31	7	(	(	PUNCT
brj-23849	31	8	gan	gan	PROPN
brj-23849	31	9	)	)	PUNCT
brj-23849	31	10	provides	provide	VERB
brj-23849	31	11	a	a	DET
brj-23849	31	12	completely	completely	ADV
brj-23849	31	13	new	new	ADJ
brj-23849	31	14	solution	solution	NOUN
brj-23849	31	15	(	(	PUNCT
brj-23849	31	16	goodfellow	goodfellow	PROPN
brj-23849	31	17	et	et	PROPN
brj-23849	31	18	al	al	PROPN
brj-23849	31	19	.	.	PROPN
brj-23849	31	20	2014	2014	NUM
brj-23849	31	21	)	)	PUNCT
brj-23849	31	22	.	.	PUNCT
brj-23849	32	1	the	the	DET
brj-23849	32	2	gan	gan	PROPN
brj-23849	32	3	has	have	AUX
brj-23849	32	4	been	be	AUX
brj-23849	32	5	continuously	continuously	ADV
brj-23849	32	6	improved	improve	VERB
brj-23849	32	7	and	and	CCONJ
brj-23849	32	8	has	have	AUX
brj-23849	32	9	been	be	AUX
brj-23849	32	10	applied	apply	VERB
brj-23849	32	11	in	in	ADP
brj-23849	32	12	many	many	ADJ
brj-23849	32	13	fields	field	NOUN
brj-23849	32	14	such	such	ADJ
brj-23849	32	15	as	as	ADP
brj-23849	32	16	generating	generate	VERB
brj-23849	32	17	audio	audio	NOUN
brj-23849	32	18	(	(	PUNCT
brj-23849	32	19	yamamoto	yamamoto	NOUN
brj-23849	32	20	et	et	PROPN
brj-23849	32	21	al	al	PROPN
brj-23849	32	22	.	.	PROPN
brj-23849	32	23	2019	2019	NUM
brj-23849	32	24	)	)	PUNCT
brj-23849	32	25	,	,	PUNCT
brj-23849	32	26	high	high	ADJ
brj-23849	32	27	resolution	resolution	NOUN
brj-23849	32	28	images	image	NOUN
brj-23849	32	29	(	(	PUNCT
brj-23849	32	30	karras	karras	X
brj-23849	32	31	et	et	PROPN
brj-23849	32	32	al	al	PROPN
brj-23849	32	33	.	.	PROPN
brj-23849	32	34	2017	2017	NUM
brj-23849	32	35	)	)	PUNCT
brj-23849	32	36	,	,	PUNCT
brj-23849	32	37	and	and	CCONJ
brj-23849	32	38	image	image	NOUN
brj-23849	32	39	style	style	NOUN
brj-23849	32	40	conversion	conversion	NOUN
brj-23849	32	41	(	(	PUNCT
brj-23849	32	42	yang	yang	PROPN
brj-23849	32	43	et	et	PROPN
brj-23849	32	44	al	al	PROPN
brj-23849	32	45	.	.	PROPN
brj-23849	32	46	2022	2022	NUM
brj-23849	32	47	)	)	PUNCT
brj-23849	32	48	.	.	PUNCT
brj-23849	33	1	in	in	ADP
brj-23849	33	2	this	this	DET
brj-23849	33	3	paper	paper	NOUN
brj-23849	33	4	,	,	PUNCT
brj-23849	33	5	an	an	DET
brj-23849	33	6	improved	improved	ADJ
brj-23849	33	7	dcgan	dcgan	NOUN
brj-23849	33	8	(	(	PUNCT
brj-23849	33	9	deep	deep	ADJ
brj-23849	33	10	convolutional	convolutional	ADJ
brj-23849	33	11	generative	generative	ADJ
brj-23849	33	12	adversarial	adversarial	ADJ
brj-23849	33	13	network	network	NOUN
brj-23849	33	14	)	)	PUNCT
brj-23849	33	15	is	be	AUX
brj-23849	33	16	purposefully	purposefully	ADV
brj-23849	33	17	designed	design	VERB
brj-23849	33	18	based	base	VERB
brj-23849	33	19	on	on	ADP
brj-23849	33	20	the	the	DET
brj-23849	33	21	characteristics	characteristic	NOUN
brj-23849	33	22	of	of	ADP
brj-23849	33	23	american	american	PROPN
brj-23849	33	24	hyphantria	hyphantria	PROPN
brj-23849	33	25	cunea	cunea	PROPN
brj-23849	33	26	larvae	larvae	PROPN
brj-23849	33	27	net	net	ADJ
brj-23849	33	28	curtain	curtain	NOUN
brj-23849	33	29	images	image	NOUN
brj-23849	33	30	,	,	PUNCT
brj-23849	33	31	which	which	PRON
brj-23849	33	32	enables	enable	VERB
brj-23849	33	33	the	the	DET
brj-23849	33	34	existing	exist	VERB
brj-23849	33	35	dataset	dataset	NOUN
brj-23849	33	36	to	to	PART
brj-23849	33	37	be	be	AUX
brj-23849	33	38	enhanced	enhance	VERB
brj-23849	33	39	,	,	PUNCT
brj-23849	33	40	and	and	CCONJ
brj-23849	33	41	the	the	DET
brj-23849	33	42	use	use	NOUN
brj-23849	33	43	of	of	ADP
brj-23849	33	44	the	the	DET
brj-23849	33	45	enhanced	enhance	VERB
brj-23849	33	46	dataset	dataset	NOUN
brj-23849	33	47	to	to	PART
brj-23849	33	48	avoid	avoid	VERB
brj-23849	33	49	the	the	DET
brj-23849	33	50	occurrence	occurrence	NOUN
brj-23849	33	51	of	of	ADP
brj-23849	33	52	overfitting	overfitte	VERB
brj-23849	33	53	during	during	ADP
brj-23849	33	54	training	training	NOUN
brj-23849	33	55	and	and	CCONJ
brj-23849	33	56	improves	improve	VERB
brj-23849	33	57	the	the	DET
brj-23849	33	58	generalization	generalization	NOUN
brj-23849	33	59	ability	ability	NOUN
brj-23849	33	60	of	of	ADP
brj-23849	33	61	the	the	DET
brj-23849	33	62	model	model	NOUN
brj-23849	33	63	.	.	PUNCT
brj-23849	34	1	experimental	experimental	ADJ
brj-23849	34	2	preparation	preparation	NOUN
brj-23849	34	3	of	of	ADP
brj-23849	34	4	the	the	DET
brj-23849	34	5	training	training	NOUN
brj-23849	34	6	set	set	VERB
brj-23849	34	7	the	the	DET
brj-23849	34	8	experimentally	experimentally	ADV
brj-23849	34	9	taken	take	VERB
brj-23849	34	10	pictures	picture	NOUN
brj-23849	34	11	of	of	ADP
brj-23849	34	12	the	the	DET
brj-23849	34	13	partly	partly	ADV
brj-23849	34	14	real	real	ADJ
brj-23849	34	15	net	net	ADJ
brj-23849	34	16	screen	screen	NOUN
brj-23849	34	17	are	be	AUX
brj-23849	34	18	shown	show	VERB
brj-23849	34	19	in	in	ADP
brj-23849	34	20	fig	fig	NOUN
brj-23849	34	21	.	.	PUNCT
brj-23849	35	1	1	1	NUM
brj-23849	35	2	,	,	PUNCT
brj-23849	35	3	and	and	CCONJ
brj-23849	35	4	the	the	DET
brj-23849	35	5	resolution	resolution	NOUN
brj-23849	35	6	of	of	ADP
brj-23849	35	7	the	the	DET
brj-23849	35	8	pictures	picture	NOUN
brj-23849	35	9	was	be	AUX
brj-23849	35	10	960	960	NUM
brj-23849	35	11	×	×	NOUN
brj-23849	35	12	720	720	NUM
brj-23849	35	13	.	.	PUNCT
brj-23849	36	1	in	in	ADP
brj-23849	36	2	this	this	DET
brj-23849	36	3	paper	paper	NOUN
brj-23849	36	4	,	,	PUNCT
brj-23849	36	5	a	a	DET
brj-23849	36	6	series	series	NOUN
brj-23849	36	7	of	of	ADP
brj-23849	36	8	real	real	ADJ
brj-23849	36	9	images	image	NOUN
brj-23849	36	10	represented	represent	VERB
brj-23849	36	11	in	in	ADP
brj-23849	36	12	fig	fig	NOUN
brj-23849	36	13	.	.	PUNCT
brj-23849	37	1	1	1	NUM
brj-23849	37	2	are	be	AUX
brj-23849	37	3	cropped	crop	VERB
brj-23849	37	4	into	into	ADP
brj-23849	37	5	thousands	thousand	NOUN
brj-23849	37	6	of	of	ADP
brj-23849	37	7	64	64	NUM
brj-23849	37	8	*	*	SYM
brj-23849	37	9	64	64	NUM
brj-23849	37	10	resolution	resolution	NOUN
brj-23849	37	11	images	image	NOUN
brj-23849	37	12	and	and	CCONJ
brj-23849	37	13	manually	manually	ADV
brj-23849	37	14	picked	pick	VERB
brj-23849	37	15	and	and	CCONJ
brj-23849	37	16	classified	classified	ADJ
brj-23849	37	17	.	.	PUNCT
brj-23849	38	1	the	the	DET
brj-23849	38	2	infected	infected	ADJ
brj-23849	38	3	leaf	leaf	NOUN
brj-23849	38	4	images	image	NOUN
brj-23849	38	5	were	be	AUX
brj-23849	38	6	sorted	sort	VERB
brj-23849	38	7	out	out	ADP
brj-23849	38	8	,	,	PUNCT
brj-23849	38	9	some	some	PRON
brj-23849	38	10	of	of	ADP
brj-23849	38	11	which	which	PRON
brj-23849	38	12	are	be	AUX
brj-23849	38	13	shown	show	VERB
brj-23849	38	14	in	in	ADP
brj-23849	38	15	fig	fig	NOUN
brj-23849	38	16	.	.	PUNCT
brj-23849	39	1	2	2	X
brj-23849	39	2	.	.	X
brj-23849	39	3	in	in	ADP
brj-23849	39	4	this	this	DET
brj-23849	39	5	paper	paper	NOUN
brj-23849	39	6	,	,	PUNCT
brj-23849	39	7	these	these	DET
brj-23849	39	8	images	image	NOUN
brj-23849	39	9	were	be	AUX
brj-23849	39	10	manually	manually	ADV
brj-23849	39	11	sorted	sort	VERB
brj-23849	39	12	again	again	ADV
brj-23849	39	13	,	,	PUNCT
brj-23849	39	14	and	and	CCONJ
brj-23849	39	15	similar	similar	ADJ
brj-23849	39	16	net	net	ADJ
brj-23849	39	17	curtain	curtain	NOUN
brj-23849	39	18	images	image	NOUN
brj-23849	39	19	were	be	AUX
brj-23849	39	20	grouped	group	VERB
brj-23849	39	21	into	into	ADP
brj-23849	39	22	a	a	DET
brj-23849	39	23	category	category	NOUN
brj-23849	39	24	.	.	PUNCT
brj-23849	40	1	there	there	PRON
brj-23849	40	2	were	be	VERB
brj-23849	40	3	12	12	NUM
brj-23849	40	4	categories	category	NOUN
brj-23849	40	5	of	of	ADP
brj-23849	40	6	american	american	PROPN
brj-23849	40	7	hyphantria	hyphantria	PROPN
brj-23849	40	8	cunea	cunea	PROPN
brj-23849	40	9	larvae	larvae	PROPN
brj-23849	40	10	net	net	ADJ
brj-23849	40	11	curtain	curtain	NOUN
brj-23849	40	12	images	image	NOUN
brj-23849	40	13	that	that	PRON
brj-23849	40	14	were	be	AUX
brj-23849	40	15	sorted	sort	VERB
brj-23849	40	16	out	out	ADP
brj-23849	40	17	.	.	PUNCT
brj-23849	41	1	the	the	DET
brj-23849	41	2	improvements	improvement	NOUN
brj-23849	41	3	of	of	ADP
brj-23849	41	4	the	the	DET
brj-23849	41	5	checkerboard	checkerboard	NOUN
brj-23849	41	6	artifacts	artifact	NOUN
brj-23849	41	7	in	in	ADP
brj-23849	41	8	the	the	DET
brj-23849	41	9	original	original	ADJ
brj-23849	41	10	dcgan	dcgan	NOUN
brj-23849	41	11	,	,	PUNCT
brj-23849	41	12	when	when	SCONJ
brj-23849	41	13	the	the	DET
brj-23849	41	14	images	image	NOUN
brj-23849	41	15	generated	generate	VERB
brj-23849	41	16	by	by	ADP
brj-23849	41	17	the	the	DET
brj-23849	41	18	deconvolution	deconvolution	NOUN
brj-23849	41	19	network	network	NOUN
brj-23849	41	20	were	be	AUX
brj-23849	41	21	carefully	carefully	ADV
brj-23849	41	22	observed	observe	VERB
brj-23849	41	23	(	(	PUNCT
brj-23849	41	24	fig	fig	NOUN
brj-23849	41	25	.	.	PUNCT
brj-23849	41	26	3(a	3(a	NUM
brj-23849	41	27	)	)	PUNCT
brj-23849	41	28	)	)	PUNCT
brj-23849	41	29	,	,	PUNCT
brj-23849	41	30	a	a	DET
brj-23849	41	31	distinct	distinct	ADJ
brj-23849	41	32	checkerboard	checkerboard	NOUN
brj-23849	41	33	artifact	artifact	NOUN
brj-23849	41	34	(	(	PUNCT
brj-23849	41	35	kingma	kingma	PROPN
brj-23849	41	36	and	and	CCONJ
brj-23849	41	37	ba	ba	PROPN
brj-23849	41	38	2014	2014	NUM
brj-23849	41	39	;	;	PUNCT
brj-23849	41	40	cao	cao	PROPN
brj-23849	41	41	et	et	PROPN
brj-23849	41	42	al	al	PROPN
brj-23849	41	43	.	.	PROPN
brj-23849	41	44	2023	2023	NUM
brj-23849	41	45	)	)	PUNCT
brj-23849	41	46	was	be	AUX
brj-23849	41	47	observed	observe	VERB
brj-23849	41	48	,	,	PUNCT
brj-23849	41	49	which	which	PRON
brj-23849	41	50	is	be	AUX
brj-23849	41	51	due	due	ADJ
brj-23849	41	52	to	to	ADP
brj-23849	41	53	the	the	DET
brj-23849	41	54	uneven	uneven	ADJ
brj-23849	41	55	and	and	CCONJ
brj-23849	41	56	overlapping	overlap	VERB
brj-23849	41	57	pixels	pixel	NOUN
brj-23849	41	58	of	of	ADP
brj-23849	41	59	the	the	DET
brj-23849	41	60	image	image	NOUN
brj-23849	41	61	caused	cause	VERB
brj-23849	41	62	by	by	ADP
brj-23849	41	63	the	the	DET
brj-23849	41	64	deconvolution	deconvolution	NOUN
brj-23849	41	65	operation	operation	NOUN
brj-23849	41	66	,	,	PUNCT
brj-23849	41	67	and	and	CCONJ
brj-23849	41	68	the	the	DET
brj-23849	41	69	visual	visual	ADJ
brj-23849	41	70	transition	transition	NOUN
brj-23849	41	71	was	be	AUX
brj-23849	41	72	not	not	PART
brj-23849	41	73	smooth	smooth	ADJ
brj-23849	41	74	due	due	ADJ
brj-23849	41	75	to	to	ADP
brj-23849	41	76	the	the	DET
brj-23849	41	77	different	different	ADJ
brj-23849	41	78	color	color	NOUN
brj-23849	41	79	shades	shade	NOUN
brj-23849	41	80	of	of	ADP
brj-23849	41	81	the	the	DET
brj-23849	41	82	adjacent	adjacent	ADJ
brj-23849	41	83	parts	part	NOUN
brj-23849	41	84	of	of	ADP
brj-23849	41	85	the	the	DET
brj-23849	41	86	image	image	NOUN
brj-23849	41	87	.	.	PUNCT
brj-23849	42	1	to	to	PART
brj-23849	42	2	alleviate	alleviate	VERB
brj-23849	42	3	the	the	DET
brj-23849	42	4	peerreviewed	peerreviewe	VERB
brj-23849	42	5	article	article	NOUN
brj-23849	42	6	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	42	7	teng	teng	PROPN
brj-23849	42	8	et	et	PROPN
brj-23849	42	9	al	al	PROPN
brj-23849	42	10	.	.	PROPN
brj-23849	43	1	(	(	PUNCT
brj-23849	43	2	2024	2024	NUM
brj-23849	43	3	)	)	PUNCT
brj-23849	43	4	.	.	PUNCT
brj-23849	44	1	“	"	PUNCT
brj-23849	44	2	dcgan	dcgan	VERB
brj-23849	44	3	enhancement	enhancement	NOUN
brj-23849	44	4	method	method	NOUN
brj-23849	44	5	,	,	PUNCT
brj-23849	44	6	”	"	PUNCT
brj-23849	44	7	bioresources	bioresource	NOUN
brj-23849	44	8	19(4	19(4	NUM
brj-23849	44	9	)	)	PUNCT
brj-23849	44	10	,	,	PUNCT
brj-23849	44	11	9271	9271	NUM
brj-23849	44	12	-	-	SYM
brj-23849	44	13	9284	9284	NUM
brj-23849	44	14	.	.	PUNCT
brj-23849	45	1	9273	9273	NUM
brj-23849	45	2	checkerboard	checkerboard	NOUN
brj-23849	45	3	effect	effect	NOUN
brj-23849	45	4	,	,	PUNCT
brj-23849	45	5	in	in	ADP
brj-23849	45	6	this	this	DET
brj-23849	45	7	work	work	NOUN
brj-23849	45	8	the	the	DET
brj-23849	45	9	deconvolution	deconvolution	NOUN
brj-23849	45	10	layer	layer	NOUN
brj-23849	45	11	was	be	AUX
brj-23849	45	12	eliminated	eliminate	VERB
brj-23849	45	13	in	in	ADP
brj-23849	45	14	the	the	DET
brj-23849	45	15	original	original	ADJ
brj-23849	45	16	dcgan	dcgan	NOUN
brj-23849	45	17	network	network	NOUN
brj-23849	45	18	and	and	CCONJ
brj-23849	45	19	instead	instead	ADV
brj-23849	45	20	a	a	DET
brj-23849	45	21	resized	resize	VERB
brj-23849	45	22	convolution	convolution	NOUN
brj-23849	45	23	layer	layer	NOUN
brj-23849	45	24	was	be	AUX
brj-23849	45	25	used	use	VERB
brj-23849	45	26	,	,	PUNCT
brj-23849	45	27	consisting	consist	VERB
brj-23849	45	28	of	of	ADP
brj-23849	45	29	an	an	DET
brj-23849	45	30	upsampling	upsample	VERB
brj-23849	45	31	2d	2d	PROPN
brj-23849	45	32	operation	operation	NOUN
brj-23849	45	33	and	and	CCONJ
brj-23849	45	34	a	a	DET
brj-23849	45	35	forward	forward	ADJ
brj-23849	45	36	convolution	convolution	NOUN
brj-23849	45	37	(	(	PUNCT
brj-23849	45	38	conv2d	conv2d	NOUN
brj-23849	45	39	)	)	PUNCT
brj-23849	45	40	operation	operation	NOUN
brj-23849	45	41	with	with	ADP
brj-23849	45	42	a	a	DET
brj-23849	45	43	step	step	NOUN
brj-23849	45	44	size	size	NOUN
brj-23849	45	45	of	of	ADP
brj-23849	45	46	1	1	NUM
brj-23849	45	47	.	.	PUNCT
brj-23849	46	1	figure	figure	NOUN
brj-23849	46	2	(	(	PUNCT
brj-23849	46	3	3	3	X
brj-23849	46	4	)	)	PUNCT
brj-23849	46	5	shows	show	VERB
brj-23849	46	6	the	the	DET
brj-23849	46	7	comparison	comparison	NOUN
brj-23849	46	8	of	of	ADP
brj-23849	46	9	the	the	DET
brj-23849	46	10	images	image	NOUN
brj-23849	46	11	generated	generate	VERB
brj-23849	46	12	by	by	ADP
brj-23849	46	13	using	use	VERB
brj-23849	46	14	resize	resize	NOUN
brj-23849	46	15	convolution	convolution	NOUN
brj-23849	46	16	and	and	CCONJ
brj-23849	46	17	deconvolution	deconvolution	NOUN
brj-23849	46	18	at	at	ADP
brj-23849	46	19	different	different	ADJ
brj-23849	46	20	epochs	epoch	NOUN
brj-23849	46	21	.	.	PUNCT
brj-23849	47	1	the	the	DET
brj-23849	47	2	training	training	NOUN
brj-23849	47	3	process	process	NOUN
brj-23849	47	4	using	use	VERB
brj-23849	47	5	the	the	DET
brj-23849	47	6	original	original	ADJ
brj-23849	47	7	deconvolution	deconvolution	NOUN
brj-23849	47	8	layer	layer	NOUN
brj-23849	47	9	for	for	ADP
brj-23849	47	10	the	the	DET
brj-23849	47	11	upsampling	upsampling	NOUN
brj-23849	47	12	operation	operation	NOUN
brj-23849	47	13	is	be	AUX
brj-23849	47	14	shown	show	VERB
brj-23849	47	15	in	in	ADP
brj-23849	47	16	fig	fig	NOUN
brj-23849	47	17	.	.	PUNCT
brj-23849	48	1	3(a	3(a	NUM
brj-23849	48	2	)	)	PUNCT
brj-23849	48	3	,	,	PUNCT
brj-23849	48	4	and	and	CCONJ
brj-23849	48	5	the	the	DET
brj-23849	48	6	training	training	NOUN
brj-23849	48	7	process	process	NOUN
brj-23849	48	8	after	after	ADP
brj-23849	48	9	changing	change	VERB
brj-23849	48	10	to	to	ADP
brj-23849	48	11	the	the	DET
brj-23849	48	12	resize	resize	NOUN
brj-23849	48	13	convolution	convolution	NOUN
brj-23849	48	14	layer	layer	NOUN
brj-23849	48	15	is	be	AUX
brj-23849	48	16	shown	show	VERB
brj-23849	48	17	in	in	ADP
brj-23849	48	18	fig	fig	NOUN
brj-23849	48	19	.	.	PUNCT
brj-23849	49	1	3(b	3(b	NUM
brj-23849	49	2	)	)	PUNCT
brj-23849	49	3	.	.	PUNCT
brj-23849	50	1	fig	fig	NOUN
brj-23849	50	2	.	.	PUNCT
brj-23849	51	1	1	1	X
brj-23849	51	2	.	.	X
brj-23849	51	3	real	real	ADJ
brj-23849	51	4	net	net	ADJ
brj-23849	51	5	screen	screen	NOUN
brj-23849	51	6	image	image	NOUN
brj-23849	51	7	fig	fig	NOUN
brj-23849	51	8	.	.	PUNCT
brj-23849	52	1	2	2	X
brj-23849	52	2	.	.	X
brj-23849	52	3	infected	infect	VERB
brj-23849	52	4	leaf	leaf	NOUN
brj-23849	52	5	epoch	epoch	NOUN
brj-23849	52	6	1	1	NUM
brj-23849	52	7	500	500	NUM
brj-23849	52	8	1000	1000	NUM
brj-23849	52	9	2000	2000	NUM
brj-23849	52	10	3000	3000	NUM
brj-23849	52	11	epoch	epoch	NOUN
brj-23849	52	12	1	1	NUM
brj-23849	52	13	500	500	NUM
brj-23849	52	14	1000	1000	NUM
brj-23849	52	15	2000	2000	NUM
brj-23849	52	16	3000	3000	NUM
brj-23849	52	17	(	(	PUNCT
brj-23849	52	18	a	a	NOUN
brj-23849	52	19	)	)	PUNCT
brj-23849	52	20	(	(	PUNCT
brj-23849	52	21	b	b	X
brj-23849	52	22	)	)	PUNCT
brj-23849	52	23	fig	fig	NOUN
brj-23849	52	24	.	.	PUNCT
brj-23849	53	1	3	3	X
brj-23849	53	2	.	.	X
brj-23849	53	3	comparison	comparison	NOUN
brj-23849	53	4	of	of	ADP
brj-23849	53	5	training	training	NOUN
brj-23849	53	6	process	process	NOUN
brj-23849	53	7	before	before	ADP
brj-23849	53	8	and	and	CCONJ
brj-23849	53	9	after	after	ADP
brj-23849	53	10	improvement	improvement	NOUN
brj-23849	53	11	:	:	PUNCT
brj-23849	53	12	(	(	PUNCT
brj-23849	53	13	a	a	X
brj-23849	53	14	)	)	PUNCT
brj-23849	53	15	images	image	NOUN
brj-23849	53	16	generated	generate	VERB
brj-23849	53	17	using	use	VERB
brj-23849	53	18	deconvolution	deconvolution	NOUN
brj-23849	53	19	at	at	ADP
brj-23849	53	20	different	different	ADJ
brj-23849	53	21	epochs	epoch	NOUN
brj-23849	53	22	,	,	PUNCT
brj-23849	53	23	and	and	CCONJ
brj-23849	53	24	(	(	PUNCT
brj-23849	53	25	b	b	NOUN
brj-23849	53	26	)	)	PUNCT
brj-23849	53	27	images	image	NOUN
brj-23849	53	28	generated	generate	VERB
brj-23849	53	29	using	use	VERB
brj-23849	53	30	resize	resize	NOUN
brj-23849	53	31	convolution	convolution	NOUN
brj-23849	53	32	at	at	ADP
brj-23849	53	33	different	different	ADJ
brj-23849	53	34	epochs	epoch	NOUN
brj-23849	53	35	table	table	NOUN
brj-23849	53	36	1	1	NUM
brj-23849	53	37	.	.	PUNCT
brj-23849	53	38	comparison	comparison	NOUN
brj-23849	53	39	of	of	ADP
brj-23849	53	40	fid	fid	NOUN
brj-23849	53	41	indicators	indicator	NOUN
brj-23849	53	42	for	for	ADP
brj-23849	53	43	using	use	VERB
brj-23849	53	44	different	different	ADJ
brj-23849	53	45	convolution	convolution	NOUN
brj-23849	53	46	methods	method	NOUN
brj-23849	53	47	using	use	VERB
brj-23849	53	48	different	different	ADJ
brj-23849	53	49	convolution	convolution	NOUN
brj-23849	53	50	methods	method	NOUN
brj-23849	53	51	fid	fid	VERB
brj-23849	53	52	score	score	NOUN
brj-23849	53	53	using	use	VERB
brj-23849	53	54	resize	resize	NOUN
brj-23849	53	55	convolution	convolution	NOUN
brj-23849	53	56	147.30	147.30	NUM
brj-23849	53	57	using	use	VERB
brj-23849	53	58	deconvolution	deconvolution	NOUN
brj-23849	53	59	288.41	288.41	NUM
brj-23849	53	60	from	from	ADP
brj-23849	53	61	table	table	NOUN
brj-23849	53	62	1	1	NUM
brj-23849	53	63	,	,	PUNCT
brj-23849	53	64	it	it	PRON
brj-23849	53	65	can	can	AUX
brj-23849	53	66	be	be	AUX
brj-23849	53	67	judged	judge	VERB
brj-23849	53	68	that	that	SCONJ
brj-23849	53	69	the	the	DET
brj-23849	53	70	use	use	NOUN
brj-23849	53	71	of	of	ADP
brj-23849	53	72	resize	resize	NOUN
brj-23849	53	73	convolution	convolution	NOUN
brj-23849	53	74	was	be	AUX
brj-23849	53	75	much	much	ADV
brj-23849	53	76	better	well	ADJ
brj-23849	53	77	than	than	ADP
brj-23849	53	78	the	the	DET
brj-23849	53	79	use	use	NOUN
brj-23849	53	80	of	of	ADP
brj-23849	53	81	deconvolution	deconvolution	NOUN
brj-23849	53	82	.	.	PUNCT
brj-23849	54	1	measures	measure	NOUN
brj-23849	54	2	related	relate	VERB
brj-23849	54	3	to	to	ADP
brj-23849	54	4	improving	improve	VERB
brj-23849	54	5	network	network	NOUN
brj-23849	54	6	stability	stability	NOUN
brj-23849	54	7	to	to	PART
brj-23849	54	8	prevent	prevent	VERB
brj-23849	54	9	the	the	DET
brj-23849	54	10	network	network	NOUN
brj-23849	54	11	from	from	ADP
brj-23849	54	12	overfitting	overfitte	VERB
brj-23849	54	13	,	,	PUNCT
brj-23849	54	14	to	to	PART
brj-23849	54	15	prevent	prevent	VERB
brj-23849	54	16	the	the	DET
brj-23849	54	17	parameters	parameter	NOUN
brj-23849	54	18	from	from	ADP
brj-23849	54	19	relying	rely	VERB
brj-23849	54	20	too	too	ADV
brj-23849	54	21	much	much	ADJ
brj-23849	54	22	on	on	ADP
brj-23849	54	23	the	the	DET
brj-23849	54	24	training	training	NOUN
brj-23849	54	25	data	datum	NOUN
brj-23849	54	26	,	,	PUNCT
brj-23849	54	27	and	and	CCONJ
brj-23849	54	28	to	to	PART
brj-23849	54	29	increase	increase	VERB
brj-23849	54	30	the	the	DET
brj-23849	54	31	generalization	generalization	NOUN
brj-23849	54	32	ability	ability	NOUN
brj-23849	54	33	of	of	ADP
brj-23849	54	34	the	the	DET
brj-23849	54	35	parameters	parameter	NOUN
brj-23849	54	36	to	to	ADP
brj-23849	54	37	the	the	DET
brj-23849	54	38	dataset	dataset	NOUN
brj-23849	54	39	,	,	PUNCT
brj-23849	54	40	dropout	dropout	NOUN
brj-23849	54	41	layers	layer	NOUN
brj-23849	54	42	were	be	AUX
brj-23849	54	43	added	add	VERB
brj-23849	54	44	to	to	ADP
brj-23849	54	45	both	both	DET
brj-23849	54	46	the	the	DET
brj-23849	54	47	generator	generator	NOUN
brj-23849	54	48	and	and	CCONJ
brj-23849	54	49	the	the	DET
brj-23849	54	50	discriminator	discriminator	NOUN
brj-23849	54	51	in	in	ADP
brj-23849	54	52	this	this	DET
brj-23849	54	53	paper	paper	NOUN
brj-23849	54	54	(	(	PUNCT
brj-23849	54	55	srivastava	srivastava	PROPN
brj-23849	54	56	et	et	PROPN
brj-23849	54	57	al	al	PROPN
brj-23849	54	58	.	.	PROPN
brj-23849	54	59	2014	2014	NUM
brj-23849	54	60	;	;	PUNCT
brj-23849	54	61	park	park	NOUN
brj-23849	54	62	and	and	CCONJ
brj-23849	54	63	kwak	kwak	PROPN
brj-23849	54	64	2016	2016	NUM
brj-23849	54	65	)	)	PUNCT
brj-23849	54	66	.	.	PUNCT
brj-23849	55	1	this	this	DET
brj-23849	55	2	paper	paper	NOUN
brj-23849	55	3	compared	compare	VERB
brj-23849	55	4	the	the	DET
brj-23849	55	5	training	training	NOUN
brj-23849	55	6	effect	effect	NOUN
brj-23849	55	7	of	of	ADP
brj-23849	55	8	adding	add	VERB
brj-23849	55	9	the	the	DET
brj-23849	55	10	dropout	dropout	NOUN
brj-23849	55	11	layer	layer	NOUN
brj-23849	55	12	and	and	CCONJ
brj-23849	55	13	not	not	PART
brj-23849	55	14	adding	add	VERB
brj-23849	55	15	the	the	DET
brj-23849	55	16	dropout	dropout	NOUN
brj-23849	55	17	layer	layer	NOUN
brj-23849	55	18	,	,	PUNCT
brj-23849	55	19	as	as	SCONJ
brj-23849	55	20	shown	show	VERB
brj-23849	55	21	in	in	ADP
brj-23849	55	22	fig	fig	NOUN
brj-23849	55	23	.	.	PUNCT
brj-23849	56	1	4	4	X
brj-23849	56	2	.	.	PUNCT
brj-23849	56	3	peerreviewed	peerreviewe	VERB
brj-23849	56	4	article	article	NOUN
brj-23849	56	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	56	6	teng	teng	PROPN
brj-23849	56	7	et	et	PROPN
brj-23849	56	8	al	al	PROPN
brj-23849	56	9	.	.	PROPN
brj-23849	57	1	(	(	PUNCT
brj-23849	57	2	2024	2024	NUM
brj-23849	57	3	)	)	PUNCT
brj-23849	57	4	.	.	PUNCT
brj-23849	58	1	“	"	PUNCT
brj-23849	58	2	dcgan	dcgan	VERB
brj-23849	58	3	enhancement	enhancement	NOUN
brj-23849	58	4	method	method	NOUN
brj-23849	58	5	,	,	PUNCT
brj-23849	58	6	”	"	PUNCT
brj-23849	58	7	bioresources	bioresource	NOUN
brj-23849	58	8	19(4	19(4	NUM
brj-23849	58	9	)	)	PUNCT
brj-23849	58	10	,	,	PUNCT
brj-23849	58	11	9271	9271	NUM
brj-23849	58	12	-	-	SYM
brj-23849	58	13	9284	9284	NUM
brj-23849	58	14	.	.	PUNCT
brj-23849	59	1	9274	9274	NUM
brj-23849	59	2	(	(	PUNCT
brj-23849	59	3	a	a	X
brj-23849	59	4	)	)	PUNCT
brj-23849	59	5	dropout	dropout	NOUN
brj-23849	59	6	layer	layer	NOUN
brj-23849	59	7	(	(	PUNCT
brj-23849	59	8	b	b	X
brj-23849	59	9	)	)	PUNCT
brj-23849	59	10	no	no	DET
brj-23849	59	11	dropout	dropout	NOUN
brj-23849	59	12	layer	layer	NOUN
brj-23849	59	13	added	add	VERB
brj-23849	59	14	fig	fig	NOUN
brj-23849	59	15	.	.	PUNCT
brj-23849	60	1	4	4	X
brj-23849	60	2	.	.	X
brj-23849	60	3	comparison	comparison	NOUN
brj-23849	60	4	of	of	ADP
brj-23849	60	5	loss	loss	NOUN
brj-23849	60	6	values	value	NOUN
brj-23849	60	7	with	with	ADP
brj-23849	60	8	(	(	PUNCT
brj-23849	60	9	a	a	NOUN
brj-23849	60	10	)	)	PUNCT
brj-23849	60	11	and	and	CCONJ
brj-23849	60	12	without	without	ADP
brj-23849	60	13	the	the	DET
brj-23849	60	14	dropout	dropout	NOUN
brj-23849	60	15	layer	layer	NOUN
brj-23849	60	16	(	(	PUNCT
brj-23849	60	17	b	b	NOUN
brj-23849	60	18	)	)	PUNCT
brj-23849	60	19	table	table	NOUN
brj-23849	60	20	2	2	NUM
brj-23849	60	21	shows	show	VERB
brj-23849	60	22	the	the	DET
brj-23849	60	23	fid	fid	NOUN
brj-23849	60	24	metric	metric	ADJ
brj-23849	60	25	score	score	NOUN
brj-23849	60	26	for	for	ADP
brj-23849	60	27	the	the	DET
brj-23849	60	28	images	image	NOUN
brj-23849	60	29	generated	generate	VERB
brj-23849	60	30	with	with	ADP
brj-23849	60	31	and	and	CCONJ
brj-23849	60	32	without	without	ADP
brj-23849	60	33	dropout	dropout	NOUN
brj-23849	60	34	.	.	PUNCT
brj-23849	60	35	table	table	NOUN
brj-23849	61	1	2	2	NUM
brj-23849	61	2	.	.	PUNCT
brj-23849	61	3	comparison	comparison	NOUN
brj-23849	61	4	of	of	ADP
brj-23849	61	5	fid	fid	NOUN
brj-23849	61	6	indicators	indicator	NOUN
brj-23849	61	7	for	for	ADP
brj-23849	61	8	using	use	VERB
brj-23849	61	9	different	different	ADJ
brj-23849	61	10	convolution	convolution	NOUN
brj-23849	61	11	methods	method	NOUN
brj-23849	61	12	whether	whether	SCONJ
brj-23849	61	13	to	to	PART
brj-23849	61	14	use	use	VERB
brj-23849	61	15	dropout	dropout	NOUN
brj-23849	61	16	fid	fid	NOUN
brj-23849	61	17	score	score	NOUN
brj-23849	61	18	using	use	VERB
brj-23849	61	19	dropout	dropout	NOUN
brj-23849	61	20	147.30	147.30	NUM
brj-23849	61	21	without	without	ADP
brj-23849	61	22	dropout	dropout	NOUN
brj-23849	61	23	148.19	148.19	NUM
brj-23849	61	24	in	in	ADP
brj-23849	61	25	this	this	DET
brj-23849	61	26	paper	paper	NOUN
brj-23849	61	27	,	,	PUNCT
brj-23849	61	28	different	different	ADJ
brj-23849	61	29	loss	loss	NOUN
brj-23849	61	30	functions	function	NOUN
brj-23849	61	31	were	be	AUX
brj-23849	61	32	used	use	VERB
brj-23849	61	33	:	:	PUNCT
brj-23849	61	34	binary	binary	PROPN
brj-23849	61	35	cross	cross	PROPN
brj-23849	61	36	entropy	entropy	PROPN
brj-23849	61	37	loss	loss	PROPN
brj-23849	61	38	,	,	PUNCT
brj-23849	61	39	categorical	categorical	PROPN
brj-23849	61	40	cross	cross	PROPN
brj-23849	61	41	entropy	entropy	PROPN
brj-23849	61	42	loss	loss	PROPN
brj-23849	61	43	,	,	PUNCT
brj-23849	61	44	kl	kl	X
brj-23849	61	45	divergence	divergence	NOUN
brj-23849	61	46	loss	loss	NOUN
brj-23849	61	47	,	,	PUNCT
brj-23849	61	48	mean	mean	ADJ
brj-23849	61	49	square	square	ADJ
brj-23849	61	50	error	error	NOUN
brj-23849	61	51	(	(	PUNCT
brj-23849	61	52	mse	mse	NOUN
brj-23849	61	53	)	)	PUNCT
brj-23849	61	54	loss	loss	NOUN
brj-23849	61	55	,	,	PUNCT
brj-23849	61	56	and	and	CCONJ
brj-23849	61	57	mean	mean	VERB
brj-23849	61	58	absolute	absolute	ADJ
brj-23849	61	59	error	error	NOUN
brj-23849	61	60	loss	loss	NOUN
brj-23849	61	61	.	.	PUNCT
brj-23849	62	1	experiments	experiment	NOUN
brj-23849	62	2	were	be	AUX
brj-23849	62	3	conducted	conduct	VERB
brj-23849	62	4	using	use	VERB
brj-23849	62	5	these	these	DET
brj-23849	62	6	loss	loss	NOUN
brj-23849	62	7	functions	function	NOUN
brj-23849	62	8	separately	separately	ADV
brj-23849	62	9	to	to	PART
brj-23849	62	10	compare	compare	VERB
brj-23849	62	11	their	their	PRON
brj-23849	62	12	effects	effect	NOUN
brj-23849	62	13	,	,	PUNCT
brj-23849	62	14	as	as	SCONJ
brj-23849	62	15	shown	show	VERB
brj-23849	62	16	in	in	ADP
brj-23849	62	17	fig	fig	NOUN
brj-23849	62	18	.	.	PUNCT
brj-23849	63	1	5	5	NUM
brj-23849	63	2	.	.	X
brj-23849	63	3	(	(	PUNCT
brj-23849	63	4	a	a	X
brj-23849	63	5	)	)	PUNCT
brj-23849	63	6	binary	binary	PROPN
brj-23849	63	7	cross	cross	PROPN
brj-23849	63	8	entropy	entropy	PROPN
brj-23849	63	9	loss	loss	PROPN
brj-23849	63	10	(	(	PUNCT
brj-23849	63	11	b	b	NOUN
brj-23849	63	12	)	)	PUNCT
brj-23849	63	13	categorical	categorical	PROPN
brj-23849	63	14	cross	cross	NOUN
brj-23849	63	15	entropy	entropy	PROPN
brj-23849	63	16	loss	loss	NOUN
brj-23849	63	17	(	(	PUNCT
brj-23849	63	18	c	c	NOUN
brj-23849	63	19	)	)	PUNCT
brj-23849	63	20	kl	kl	NOUN
brj-23849	63	21	divergence	divergence	NOUN
brj-23849	63	22	loss	loss	NOUN
brj-23849	63	23	(	(	PUNCT
brj-23849	63	24	d	d	NOUN
brj-23849	63	25	)	)	PUNCT
brj-23849	63	26	mean	mean	ADJ
brj-23849	63	27	square	square	ADJ
brj-23849	63	28	error	error	NOUN
brj-23849	63	29	loss	loss	NOUN
brj-23849	63	30	(	(	PUNCT
brj-23849	63	31	e	e	NOUN
brj-23849	63	32	)	)	PUNCT
brj-23849	63	33	mean	mean	VERB
brj-23849	63	34	absolute	absolute	ADJ
brj-23849	63	35	error	error	NOUN
brj-23849	63	36	loss	loss	NOUN
brj-23849	63	37	fig	fig	NOUN
brj-23849	63	38	.	.	PUNCT
brj-23849	64	1	5	5	NUM
brj-23849	64	2	.	.	X
brj-23849	64	3	comparison	comparison	NOUN
brj-23849	64	4	of	of	ADP
brj-23849	64	5	the	the	DET
brj-23849	64	6	effect	effect	NOUN
brj-23849	64	7	of	of	ADP
brj-23849	64	8	different	different	ADJ
brj-23849	64	9	loss	loss	NOUN
brj-23849	64	10	functions	function	NOUN
brj-23849	64	11	from	from	ADP
brj-23849	64	12	fig	fig	NOUN
brj-23849	64	13	.	.	PUNCT
brj-23849	65	1	5	5	NUM
brj-23849	65	2	,	,	PUNCT
brj-23849	65	3	all	all	DET
brj-23849	65	4	the	the	DET
brj-23849	65	5	loss	loss	NOUN
brj-23849	65	6	functions	function	NOUN
brj-23849	65	7	except	except	SCONJ
brj-23849	65	8	the	the	DET
brj-23849	65	9	mse	mse	NOUN
brj-23849	65	10	loss	loss	NOUN
brj-23849	65	11	function	function	NOUN
brj-23849	65	12	showed	show	VERB
brj-23849	65	13	gradient	gradient	ADJ
brj-23849	65	14	disappearance	disappearance	NOUN
brj-23849	65	15	when	when	SCONJ
brj-23849	65	16	applied	apply	VERB
brj-23849	65	17	.	.	PUNCT
brj-23849	66	1	initially	initially	ADV
brj-23849	66	2	,	,	PUNCT
brj-23849	66	3	it	it	PRON
brj-23849	66	4	was	be	AUX
brj-23849	66	5	thought	think	VERB
brj-23849	66	6	that	that	SCONJ
brj-23849	66	7	the	the	DET
brj-23849	66	8	learning	learning	NOUN
brj-23849	66	9	rate	rate	NOUN
brj-23849	66	10	was	be	AUX
brj-23849	66	11	not	not	PART
brj-23849	66	12	adjusted	adjust	VERB
brj-23849	66	13	peerreviewed	peerreviewe	VERB
brj-23849	66	14	article	article	NOUN
brj-23849	66	15	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	66	16	teng	teng	PROPN
brj-23849	66	17	et	et	PROPN
brj-23849	66	18	al	al	PROPN
brj-23849	66	19	.	.	PROPN
brj-23849	67	1	(	(	PUNCT
brj-23849	67	2	2024	2024	NUM
brj-23849	67	3	)	)	PUNCT
brj-23849	67	4	.	.	PUNCT
brj-23849	68	1	“	"	PUNCT
brj-23849	68	2	dcgan	dcgan	VERB
brj-23849	68	3	enhancement	enhancement	NOUN
brj-23849	68	4	method	method	NOUN
brj-23849	68	5	,	,	PUNCT
brj-23849	68	6	”	"	PUNCT
brj-23849	68	7	bioresources	bioresource	NOUN
brj-23849	68	8	19(4	19(4	NUM
brj-23849	68	9	)	)	PUNCT
brj-23849	68	10	,	,	PUNCT
brj-23849	68	11	9271	9271	NUM
brj-23849	68	12	-	-	SYM
brj-23849	68	13	9284	9284	NUM
brj-23849	68	14	.	.	PUNCT
brj-23849	69	1	9275	9275	NUM
brj-23849	69	2	properly	properly	ADV
brj-23849	69	3	,	,	PUNCT
brj-23849	69	4	which	which	PRON
brj-23849	69	5	led	lead	VERB
brj-23849	69	6	to	to	ADP
brj-23849	69	7	anomalies	anomaly	NOUN
brj-23849	69	8	in	in	ADP
brj-23849	69	9	the	the	DET
brj-23849	69	10	training	training	NOUN
brj-23849	69	11	process	process	NOUN
brj-23849	69	12	when	when	SCONJ
brj-23849	69	13	using	use	VERB
brj-23849	69	14	other	other	ADJ
brj-23849	69	15	loss	loss	NOUN
brj-23849	69	16	functions	function	NOUN
brj-23849	69	17	,	,	PUNCT
brj-23849	69	18	but	but	CCONJ
brj-23849	69	19	after	after	ADP
brj-23849	69	20	adjusting	adjust	VERB
brj-23849	69	21	the	the	DET
brj-23849	69	22	learning	learning	NOUN
brj-23849	69	23	rate	rate	NOUN
brj-23849	69	24	several	several	ADJ
brj-23849	69	25	times	time	NOUN
brj-23849	69	26	,	,	PUNCT
brj-23849	69	27	it	it	PRON
brj-23849	69	28	was	be	AUX
brj-23849	69	29	found	find	VERB
brj-23849	69	30	that	that	SCONJ
brj-23849	69	31	the	the	DET
brj-23849	69	32	size	size	NOUN
brj-23849	69	33	of	of	ADP
brj-23849	69	34	the	the	DET
brj-23849	69	35	learning	learning	NOUN
brj-23849	69	36	rate	rate	NOUN
brj-23849	69	37	does	do	AUX
brj-23849	69	38	not	not	PART
brj-23849	69	39	affect	affect	VERB
brj-23849	69	40	the	the	DET
brj-23849	69	41	result	result	NOUN
brj-23849	69	42	of	of	ADP
brj-23849	69	43	its	its	PRON
brj-23849	69	44	gradient	gradient	NOUN
brj-23849	69	45	vanishing	vanishing	NOUN
brj-23849	69	46	.	.	PUNCT
brj-23849	70	1	however	however	ADV
brj-23849	70	2	,	,	PUNCT
brj-23849	70	3	by	by	ADP
brj-23849	70	4	comparing	compare	VERB
brj-23849	70	5	the	the	DET
brj-23849	70	6	use	use	NOUN
brj-23849	70	7	of	of	ADP
brj-23849	70	8	mse	mse	NOUN
brj-23849	70	9	loss	loss	NOUN
brj-23849	70	10	with	with	ADP
brj-23849	70	11	binary	binary	PROPN
brj-23849	70	12	cross	cross	PROPN
brj-23849	70	13	entropy	entropy	PROPN
brj-23849	70	14	loss	loss	PROPN
brj-23849	70	15	,	,	PUNCT
brj-23849	70	16	it	it	PRON
brj-23849	70	17	was	be	AUX
brj-23849	70	18	not	not	PART
brj-23849	70	19	difficult	difficult	ADJ
brj-23849	70	20	to	to	PART
brj-23849	70	21	find	find	VERB
brj-23849	70	22	that	that	SCONJ
brj-23849	70	23	when	when	SCONJ
brj-23849	70	24	using	use	VERB
brj-23849	70	25	mse	mse	NOUN
brj-23849	70	26	loss	loss	NOUN
brj-23849	70	27	,	,	PUNCT
brj-23849	70	28	the	the	DET
brj-23849	70	29	loss	loss	NOUN
brj-23849	70	30	value	value	NOUN
brj-23849	70	31	of	of	ADP
brj-23849	70	32	the	the	DET
brj-23849	70	33	generator	generator	NOUN
brj-23849	70	34	has	have	VERB
brj-23849	70	35	two	two	NUM
brj-23849	70	36	large	large	ADJ
brj-23849	70	37	abrupt	abrupt	ADJ
brj-23849	70	38	changes	change	NOUN
brj-23849	70	39	,	,	PUNCT
brj-23849	70	40	which	which	PRON
brj-23849	70	41	indicates	indicate	VERB
brj-23849	70	42	the	the	DET
brj-23849	70	43	network	network	NOUN
brj-23849	70	44	is	be	AUX
brj-23849	70	45	less	less	ADV
brj-23849	70	46	stable	stable	ADJ
brj-23849	70	47	.	.	PUNCT
brj-23849	71	1	as	as	SCONJ
brj-23849	71	2	shown	show	VERB
brj-23849	71	3	in	in	ADP
brj-23849	71	4	fig	fig	NOUN
brj-23849	71	5	.	.	PUNCT
brj-23849	72	1	6	6	NUM
brj-23849	72	2	,	,	PUNCT
brj-23849	72	3	it	it	PRON
brj-23849	72	4	is	be	AUX
brj-23849	72	5	also	also	ADV
brj-23849	72	6	clear	clear	ADJ
brj-23849	72	7	from	from	ADP
brj-23849	72	8	its	its	PRON
brj-23849	72	9	training	training	NOUN
brj-23849	72	10	process	process	NOUN
brj-23849	72	11	that	that	PRON
brj-23849	72	12	the	the	DET
brj-23849	72	13	quality	quality	NOUN
brj-23849	72	14	of	of	ADP
brj-23849	72	15	the	the	DET
brj-23849	72	16	generated	generate	VERB
brj-23849	72	17	images	image	NOUN
brj-23849	72	18	was	be	AUX
brj-23849	72	19	very	very	ADV
brj-23849	72	20	poor	poor	ADJ
brj-23849	72	21	when	when	SCONJ
brj-23849	72	22	the	the	DET
brj-23849	72	23	epoch	epoch	NOUN
brj-23849	72	24	was	be	AUX
brj-23849	72	25	either	either	PRON
brj-23849	72	26	3200	3200	NUM
brj-23849	72	27	or	or	CCONJ
brj-23849	72	28	4800	4800	NUM
brj-23849	72	29	.	.	PUNCT
brj-23849	73	1	as	as	SCONJ
brj-23849	73	2	shown	show	VERB
brj-23849	73	3	in	in	ADP
brj-23849	73	4	table	table	NOUN
brj-23849	73	5	3	3	NUM
brj-23849	73	6	,	,	PUNCT
brj-23849	73	7	by	by	ADP
brj-23849	73	8	comparing	compare	VERB
brj-23849	73	9	the	the	DET
brj-23849	73	10	fid	fid	NOUN
brj-23849	73	11	metric	metric	ADJ
brj-23849	73	12	score	score	NOUN
brj-23849	73	13	of	of	ADP
brj-23849	73	14	the	the	DET
brj-23849	73	15	generated	generate	VERB
brj-23849	73	16	images	image	NOUN
brj-23849	73	17	using	use	VERB
brj-23849	73	18	mse	mse	NOUN
brj-23849	73	19	loss	loss	NOUN
brj-23849	73	20	and	and	CCONJ
brj-23849	73	21	binary	binary	PROPN
brj-23849	73	22	cross	cross	PROPN
brj-23849	73	23	entropy	entropy	PROPN
brj-23849	73	24	loss	loss	NOUN
brj-23849	73	25	it	it	PRON
brj-23849	73	26	is	be	AUX
brj-23849	73	27	also	also	ADV
brj-23849	73	28	evident	evident	ADJ
brj-23849	73	29	that	that	SCONJ
brj-23849	73	30	using	use	VERB
brj-23849	73	31	binary	binary	PROPN
brj-23849	73	32	cross	cross	PROPN
brj-23849	73	33	entropy	entropy	PROPN
brj-23849	73	34	loss	loss	PROPN
brj-23849	73	35	worked	work	VERB
brj-23849	73	36	well	well	ADV
brj-23849	73	37	.	.	PUNCT
brj-23849	74	1	table	table	NOUN
brj-23849	75	1	3	3	NUM
brj-23849	75	2	.	.	PUNCT
brj-23849	75	3	comparison	comparison	NOUN
brj-23849	75	4	of	of	ADP
brj-23849	75	5	is	be	AUX
brj-23849	75	6	metrics	metric	NOUN
brj-23849	75	7	and	and	CCONJ
brj-23849	75	8	fid	fid	VERB
brj-23849	75	9	metrics	metric	NOUN
brj-23849	75	10	for	for	ADP
brj-23849	75	11	images	image	NOUN
brj-23849	75	12	generated	generate	VERB
brj-23849	75	13	by	by	ADP
brj-23849	75	14	different	different	ADJ
brj-23849	75	15	loss	loss	NOUN
brj-23849	75	16	functions	function	NOUN
brj-23849	75	17	after	after	ADP
brj-23849	75	18	the	the	DET
brj-23849	75	19	above	above	ADJ
brj-23849	75	20	comparison	comparison	NOUN
brj-23849	75	21	,	,	PUNCT
brj-23849	75	22	the	the	DET
brj-23849	75	23	loss	loss	NOUN
brj-23849	75	24	function	function	NOUN
brj-23849	75	25	was	be	AUX
brj-23849	75	26	finally	finally	ADV
brj-23849	75	27	determined	determine	VERB
brj-23849	75	28	as	as	ADP
brj-23849	75	29	the	the	DET
brj-23849	75	30	binary	binary	PROPN
brj-23849	75	31	cross	cross	PROPN
brj-23849	75	32	entropy	entropy	PROPN
brj-23849	75	33	loss	loss	PROPN
brj-23849	75	34	.	.	PUNCT
brj-23849	76	1	this	this	DET
brj-23849	76	2	paper	paper	NOUN
brj-23849	76	3	used	use	VERB
brj-23849	76	4	the	the	DET
brj-23849	76	5	adam	adam	PROPN
brj-23849	76	6	(	(	PUNCT
brj-23849	76	7	kingma	kingma	PROPN
brj-23849	76	8	and	and	CCONJ
brj-23849	76	9	ba	ba	PROPN
brj-23849	76	10	2014	2014	NUM
brj-23849	76	11	;	;	PUNCT
brj-23849	76	12	cao	cao	PROPN
brj-23849	76	13	et	et	PROPN
brj-23849	76	14	al	al	PROPN
brj-23849	76	15	.	.	PROPN
brj-23849	76	16	2023	2023	NUM
brj-23849	76	17	)	)	PUNCT
brj-23849	76	18	algorithm	algorithm	NOUN
brj-23849	76	19	to	to	PART
brj-23849	76	20	update	update	VERB
brj-23849	76	21	the	the	DET
brj-23849	76	22	parameters	parameter	NOUN
brj-23849	76	23	.	.	PUNCT
brj-23849	77	1	to	to	PART
brj-23849	77	2	verify	verify	VERB
brj-23849	77	3	the	the	DET
brj-23849	77	4	effect	effect	NOUN
brj-23849	77	5	of	of	ADP
brj-23849	77	6	using	use	VERB
brj-23849	77	7	leakyrelu	leakyrelu	NOUN
brj-23849	77	8	and	and	CCONJ
brj-23849	77	9	relu	relu	NOUN
brj-23849	77	10	,	,	PUNCT
brj-23849	77	11	the	the	DET
brj-23849	77	12	procedure	procedure	NOUN
brj-23849	77	13	was	be	AUX
brj-23849	77	14	modified	modify	VERB
brj-23849	77	15	on	on	ADP
brj-23849	77	16	the	the	DET
brj-23849	77	17	activation	activation	NOUN
brj-23849	77	18	function	function	NOUN
brj-23849	77	19	only	only	ADV
brj-23849	77	20	,	,	PUNCT
brj-23849	77	21	and	and	CCONJ
brj-23849	77	22	the	the	DET
brj-23849	77	23	training	training	NOUN
brj-23849	77	24	results	result	NOUN
brj-23849	77	25	are	be	AUX
brj-23849	77	26	shown	show	VERB
brj-23849	77	27	in	in	ADP
brj-23849	77	28	fig	fig	NOUN
brj-23849	77	29	.	.	PUNCT
brj-23849	78	1	7	7	X
brj-23849	78	2	.	.	X
brj-23849	78	3	figure	figure	NOUN
brj-23849	78	4	7(a	7(a	NUM
brj-23849	78	5	)	)	PUNCT
brj-23849	78	6	shows	show	VERB
brj-23849	78	7	the	the	DET
brj-23849	78	8	image	image	NOUN
brj-23849	78	9	generated	generate	VERB
brj-23849	78	10	when	when	SCONJ
brj-23849	78	11	both	both	PRON
brj-23849	78	12	the	the	DET
brj-23849	78	13	generator	generator	NOUN
brj-23849	78	14	and	and	CCONJ
brj-23849	78	15	discriminator	discriminator	NOUN
brj-23849	78	16	used	use	VERB
brj-23849	78	17	the	the	DET
brj-23849	78	18	relu	relu	NOUN
brj-23849	78	19	function	function	NOUN
brj-23849	78	20	,	,	PUNCT
brj-23849	78	21	and	and	CCONJ
brj-23849	78	22	fig	fig	NOUN
brj-23849	78	23	.	.	PUNCT
brj-23849	79	1	7(b	7(b	X
brj-23849	79	2	)	)	PUNCT
brj-23849	79	3	shows	show	VERB
brj-23849	79	4	the	the	DET
brj-23849	79	5	image	image	NOUN
brj-23849	79	6	generated	generate	VERB
brj-23849	79	7	when	when	SCONJ
brj-23849	79	8	both	both	PRON
brj-23849	79	9	the	the	DET
brj-23849	79	10	generator	generator	NOUN
brj-23849	79	11	and	and	CCONJ
brj-23849	79	12	discriminator	discriminator	NOUN
brj-23849	79	13	used	use	VERB
brj-23849	79	14	the	the	DET
brj-23849	79	15	leakyrelu	leakyrelu	ADJ
brj-23849	79	16	function	function	NOUN
brj-23849	79	17	.	.	PUNCT
brj-23849	80	1	the	the	DET
brj-23849	80	2	change	change	NOUN
brj-23849	80	3	in	in	ADP
brj-23849	80	4	loss	loss	NOUN
brj-23849	80	5	values	value	NOUN
brj-23849	80	6	when	when	SCONJ
brj-23849	80	7	using	use	VERB
brj-23849	80	8	the	the	DET
brj-23849	80	9	leakyrelu	leakyrelu	ADJ
brj-23849	80	10	function	function	NOUN
brj-23849	80	11	and	and	CCONJ
brj-23849	80	12	when	when	SCONJ
brj-23849	80	13	using	use	VERB
brj-23849	80	14	the	the	DET
brj-23849	80	15	relu	relu	NOUN
brj-23849	80	16	function	function	NOUN
brj-23849	80	17	is	be	AUX
brj-23849	80	18	shown	show	VERB
brj-23849	80	19	in	in	ADP
brj-23849	80	20	fig	fig	NOUN
brj-23849	80	21	.	.	PUNCT
brj-23849	81	1	8	8	NUM
brj-23849	81	2	.	.	X
brj-23849	82	1	as	as	SCONJ
brj-23849	82	2	shown	show	VERB
brj-23849	82	3	in	in	ADP
brj-23849	82	4	fig	fig	NOUN
brj-23849	82	5	.	.	PUNCT
brj-23849	83	1	8	8	NUM
brj-23849	83	2	,	,	PUNCT
brj-23849	83	3	the	the	DET
brj-23849	83	4	training	training	NOUN
brj-23849	83	5	process	process	NOUN
brj-23849	83	6	of	of	ADP
brj-23849	83	7	the	the	DET
brj-23849	83	8	neural	neural	ADJ
brj-23849	83	9	network	network	NOUN
brj-23849	83	10	was	be	AUX
brj-23849	83	11	more	more	ADV
brj-23849	83	12	stable	stable	ADJ
brj-23849	83	13	when	when	SCONJ
brj-23849	83	14	using	use	VERB
brj-23849	83	15	the	the	DET
brj-23849	83	16	leakyrelu	leakyrelu	ADJ
brj-23849	83	17	function	function	NOUN
brj-23849	83	18	.	.	PUNCT
brj-23849	84	1	however	however	ADV
brj-23849	84	2	,	,	PUNCT
brj-23849	84	3	when	when	SCONJ
brj-23849	84	4	using	use	VERB
brj-23849	84	5	the	the	DET
brj-23849	84	6	relu	relu	NOUN
brj-23849	84	7	function	function	NOUN
brj-23849	84	8	,	,	PUNCT
brj-23849	84	9	images	image	NOUN
brj-23849	84	10	that	that	PRON
brj-23849	84	11	were	be	AUX
brj-23849	84	12	different	different	ADJ
brj-23849	84	13	from	from	ADP
brj-23849	84	14	the	the	DET
brj-23849	84	15	training	training	NOUN
brj-23849	84	16	data	datum	NOUN
brj-23849	84	17	but	but	CCONJ
brj-23849	84	18	matched	match	VERB
brj-23849	84	19	the	the	DET
brj-23849	84	20	characteristics	characteristic	NOUN
brj-23849	84	21	of	of	ADP
brj-23849	84	22	the	the	DET
brj-23849	84	23	american	american	PROPN
brj-23849	84	24	white	white	PROPN
brj-23849	84	25	moth	moth	PROPN
brj-23849	84	26	screen	screen	NOUN
brj-23849	84	27	image	image	NOUN
brj-23849	84	28	were	be	AUX
brj-23849	84	29	generated	generate	VERB
brj-23849	84	30	,	,	PUNCT
brj-23849	84	31	as	as	SCONJ
brj-23849	84	32	shown	show	VERB
brj-23849	84	33	in	in	ADP
brj-23849	84	34	fig	fig	NOUN
brj-23849	84	35	.	.	PUNCT
brj-23849	85	1	9	9	X
brj-23849	85	2	.	.	X
brj-23849	86	1	in	in	ADP
brj-23849	86	2	other	other	ADJ
brj-23849	86	3	words	word	NOUN
brj-23849	86	4	,	,	PUNCT
brj-23849	86	5	training	training	NOUN
brj-23849	86	6	using	use	VERB
brj-23849	86	7	the	the	DET
brj-23849	86	8	relu	relu	NOUN
brj-23849	86	9	function	function	NOUN
brj-23849	86	10	not	not	PART
brj-23849	86	11	only	only	ADV
brj-23849	86	12	expanded	expand	VERB
brj-23849	86	13	the	the	DET
brj-23849	86	14	number	number	NOUN
brj-23849	86	15	of	of	ADP
brj-23849	86	16	datasets	dataset	NOUN
brj-23849	86	17	,	,	PUNCT
brj-23849	86	18	but	but	CCONJ
brj-23849	86	19	also	also	ADV
brj-23849	86	20	expanded	expand	VERB
brj-23849	86	21	the	the	DET
brj-23849	86	22	variety	variety	NOUN
brj-23849	86	23	of	of	ADP
brj-23849	86	24	datasets	dataset	NOUN
brj-23849	86	25	.	.	PUNCT
brj-23849	87	1	in	in	ADP
brj-23849	87	2	this	this	DET
brj-23849	87	3	paper	paper	NOUN
brj-23849	87	4	,	,	PUNCT
brj-23849	87	5	leakyrelu	leakyrelu	NOUN
brj-23849	87	6	and	and	CCONJ
brj-23849	87	7	relu	relu	NOUN
brj-23849	87	8	functions	function	NOUN
brj-23849	87	9	were	be	AUX
brj-23849	87	10	used	use	VERB
brj-23849	87	11	to	to	PART
brj-23849	87	12	train	train	VERB
brj-23849	87	13	and	and	CCONJ
brj-23849	87	14	collect	collect	VERB
brj-23849	87	15	the	the	DET
brj-23849	87	16	final	final	ADJ
brj-23849	87	17	resulting	result	VERB
brj-23849	87	18	qualified	qualified	ADJ
brj-23849	87	19	images	image	NOUN
brj-23849	87	20	,	,	PUNCT
brj-23849	87	21	respectively	respectively	ADV
brj-23849	87	22	.	.	PUNCT
brj-23849	88	1	next	next	ADJ
brj-23849	88	2	,	,	PUNCT
brj-23849	88	3	bn	bn	NUM
brj-23849	88	4	layers	layer	NOUN
brj-23849	88	5	were	be	AUX
brj-23849	88	6	added	add	VERB
brj-23849	88	7	at	at	ADP
brj-23849	88	8	different	different	ADJ
brj-23849	88	9	locations	location	NOUN
brj-23849	88	10	of	of	ADP
brj-23849	88	11	the	the	DET
brj-23849	88	12	generator	generator	NOUN
brj-23849	88	13	and	and	CCONJ
brj-23849	88	14	discriminator	discriminator	NOUN
brj-23849	88	15	to	to	PART
brj-23849	88	16	further	far	ADV
brj-23849	88	17	explore	explore	VERB
brj-23849	88	18	the	the	DET
brj-23849	88	19	impact	impact	NOUN
brj-23849	88	20	of	of	ADP
brj-23849	88	21	bn	bn	ADJ
brj-23849	88	22	layers	layer	NOUN
brj-23849	88	23	.	.	PUNCT
brj-23849	89	1	adding	add	VERB
brj-23849	89	2	an	an	DET
brj-23849	89	3	upsampling	upsample	VERB
brj-23849	89	4	layer	layer	NOUN
brj-23849	89	5	to	to	ADP
brj-23849	89	6	the	the	DET
brj-23849	89	7	activation	activation	NOUN
brj-23849	89	8	function	function	NOUN
brj-23849	89	9	was	be	AUX
brj-23849	89	10	considered	consider	VERB
brj-23849	89	11	.	.	PUNCT
brj-23849	90	1	epoch	epoch	NOUN
brj-23849	90	2	0	0	NUM
brj-23849	90	3	1200	1200	NUM
brj-23849	90	4	3200	3200	NUM
brj-23849	90	5	4200	4200	NUM
brj-23849	90	6	4800	4800	NUM
brj-23849	90	7	5200	5200	NUM
brj-23849	90	8	epoch	epoch	NOUN
brj-23849	90	9	0	0	NUM
brj-23849	90	10	1200	1200	NUM
brj-23849	90	11	3200	3200	NUM
brj-23849	90	12	4200	4200	NUM
brj-23849	90	13	4800	4800	NUM
brj-23849	90	14	5200	5200	NUM
brj-23849	90	15	fig	fig	NOUN
brj-23849	90	16	.	.	PUNCT
brj-23849	91	1	6	6	NUM
brj-23849	91	2	.	.	NUM
brj-23849	91	3	images	image	NOUN
brj-23849	91	4	are	be	AUX
brj-23849	91	5	generated	generate	VERB
brj-23849	91	6	using	use	VERB
brj-23849	91	7	different	different	ADJ
brj-23849	91	8	loss	loss	NOUN
brj-23849	91	9	functions	function	NOUN
brj-23849	91	10	using	use	VERB
brj-23849	91	11	different	different	ADJ
brj-23849	91	12	loss	loss	NOUN
brj-23849	91	13	functions	function	NOUN
brj-23849	91	14	fid	fid	NOUN
brj-23849	91	15	score	score	NOUN
brj-23849	91	16	using	use	VERB
brj-23849	91	17	mse	mse	NOUN
brj-23849	91	18	loss	loss	NOUN
brj-23849	91	19	264.2	264.2	NUM
brj-23849	91	20	using	use	VERB
brj-23849	91	21	binary	binary	PROPN
brj-23849	91	22	cross	cross	PROPN
brj-23849	91	23	entropy	entropy	PROPN
brj-23849	91	24	loss	loss	PROPN
brj-23849	91	25	251.7	251.7	NUM
brj-23849	91	26	peerreviewed	peerreviewe	VERB
brj-23849	91	27	article	article	NOUN
brj-23849	91	28	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	91	29	teng	teng	PROPN
brj-23849	91	30	et	et	PROPN
brj-23849	91	31	al	al	PROPN
brj-23849	91	32	.	.	PROPN
brj-23849	92	1	(	(	PUNCT
brj-23849	92	2	2024	2024	NUM
brj-23849	92	3	)	)	PUNCT
brj-23849	92	4	.	.	PUNCT
brj-23849	93	1	“	"	PUNCT
brj-23849	93	2	dcgan	dcgan	VERB
brj-23849	93	3	enhancement	enhancement	NOUN
brj-23849	93	4	method	method	NOUN
brj-23849	93	5	,	,	PUNCT
brj-23849	93	6	”	"	PUNCT
brj-23849	93	7	bioresources	bioresource	NOUN
brj-23849	93	8	19(4	19(4	NUM
brj-23849	93	9	)	)	PUNCT
brj-23849	93	10	,	,	PUNCT
brj-23849	93	11	9271	9271	NUM
brj-23849	93	12	-	-	SYM
brj-23849	93	13	9284	9284	NUM
brj-23849	93	14	.	.	PUNCT
brj-23849	94	1	9276	9276	NUM
brj-23849	94	2	epoch	epoch	NOUN
brj-23849	94	3	0	0	NUM
brj-23849	94	4	100	100	NUM
brj-23849	94	5	500	500	NUM
brj-23849	94	6	1000	1000	NUM
brj-23849	94	7	1500	1500	NUM
brj-23849	94	8	2000	2000	NUM
brj-23849	94	9	2500	2500	NUM
brj-23849	94	10	3000	3000	NUM
brj-23849	94	11	3500	3500	NUM
brj-23849	94	12	4000	4000	NUM
brj-23849	94	13	(	(	PUNCT
brj-23849	94	14	a	a	X
brj-23849	94	15	)	)	PUNCT
brj-23849	94	16	relu	relu	NOUN
brj-23849	94	17	function	function	NOUN
brj-23849	94	18	is	be	AUX
brj-23849	94	19	used	use	VERB
brj-23849	94	20	both	both	PRON
brj-23849	94	21	in	in	ADP
brj-23849	94	22	generators	generator	NOUN
brj-23849	94	23	and	and	CCONJ
brj-23849	94	24	discriminators	discriminator	NOUN
brj-23849	94	25	epoch	epoch	PROPN
brj-23849	94	26	0	0	NUM
brj-23849	95	1	100	100	NUM
brj-23849	95	2	500	500	NUM
brj-23849	95	3	1000	1000	NUM
brj-23849	95	4	1500	1500	NUM
brj-23849	95	5	2000	2000	NUM
brj-23849	95	6	2500	2500	NUM
brj-23849	95	7	3000	3000	NUM
brj-23849	95	8	3500	3500	NUM
brj-23849	95	9	4000	4000	NUM
brj-23849	95	10	(	(	PUNCT
brj-23849	95	11	b	b	NOUN
brj-23849	95	12	)	)	PUNCT
brj-23849	95	13	leakyrelu	leakyrelu	ADJ
brj-23849	95	14	function	function	NOUN
brj-23849	95	15	is	be	AUX
brj-23849	95	16	used	use	VERB
brj-23849	95	17	both	both	PRON
brj-23849	95	18	in	in	ADP
brj-23849	95	19	generators	generator	NOUN
brj-23849	95	20	and	and	CCONJ
brj-23849	95	21	discriminators	discriminator	NOUN
brj-23849	95	22	fig	fig	NOUN
brj-23849	95	23	.	.	PUNCT
brj-23849	96	1	7	7	X
brj-23849	96	2	.	.	X
brj-23849	96	3	comparison	comparison	NOUN
brj-23849	96	4	of	of	ADP
brj-23849	96	5	the	the	DET
brj-23849	96	6	training	training	NOUN
brj-23849	96	7	process	process	NOUN
brj-23849	96	8	using	use	VERB
brj-23849	96	9	relu	relu	NOUN
brj-23849	96	10	and	and	CCONJ
brj-23849	96	11	leakyrelu	leakyrelu	NOUN
brj-23849	96	12	(	(	PUNCT
brj-23849	96	13	a	a	X
brj-23849	96	14	)	)	PUNCT
brj-23849	96	15	using	use	VERB
brj-23849	96	16	leakyrelu	leakyrelu	ADJ
brj-23849	96	17	function	function	NOUN
brj-23849	96	18	(	(	PUNCT
brj-23849	96	19	b	b	NOUN
brj-23849	96	20	)	)	PUNCT
brj-23849	96	21	using	use	VERB
brj-23849	96	22	relu	relu	NOUN
brj-23849	96	23	function	function	NOUN
brj-23849	96	24	fig	fig	NOUN
brj-23849	96	25	.	.	PUNCT
brj-23849	97	1	8	8	NUM
brj-23849	97	2	.	.	X
brj-23849	97	3	comparison	comparison	NOUN
brj-23849	97	4	of	of	ADP
brj-23849	97	5	loss	loss	NOUN
brj-23849	97	6	values	value	NOUN
brj-23849	97	7	trained	train	VERB
brj-23849	97	8	with	with	ADP
brj-23849	97	9	leakyrelu	leakyrelu	ADJ
brj-23849	97	10	function	function	NOUN
brj-23849	97	11	and	and	CCONJ
brj-23849	97	12	relu	relu	NOUN
brj-23849	97	13	function	function	NOUN
brj-23849	97	14	fig	fig	NOUN
brj-23849	97	15	.	.	PUNCT
brj-23849	98	1	9	9	X
brj-23849	98	2	.	.	PUNCT
brj-23849	98	3	generated	generate	VERB
brj-23849	98	4	images	image	NOUN
brj-23849	98	5	at	at	ADP
brj-23849	98	6	training	training	NOUN
brj-23849	98	7	time	time	NOUN
brj-23849	98	8	using	use	VERB
brj-23849	98	9	the	the	DET
brj-23849	98	10	relu	relu	NOUN
brj-23849	98	11	function	function	NOUN
brj-23849	98	12	the	the	DET
brj-23849	98	13	generator	generator	NOUN
brj-23849	98	14	was	be	AUX
brj-23849	98	15	divided	divide	VERB
brj-23849	98	16	into	into	ADP
brj-23849	98	17	multiple	multiple	ADJ
brj-23849	98	18	modules	module	NOUN
brj-23849	98	19	.	.	PUNCT
brj-23849	99	1	the	the	DET
brj-23849	99	2	convolution	convolution	NOUN
brj-23849	99	3	layer	layer	NOUN
brj-23849	99	4	and	and	CCONJ
brj-23849	99	5	the	the	DET
brj-23849	99	6	activation	activation	NOUN
brj-23849	99	7	function	function	NOUN
brj-23849	99	8	layer	layer	NOUN
brj-23849	99	9	of	of	ADP
brj-23849	99	10	the	the	DET
brj-23849	99	11	discriminator	discriminator	NOUN
brj-23849	99	12	were	be	AUX
brj-23849	99	13	treated	treat	VERB
brj-23849	99	14	as	as	ADP
brj-23849	99	15	two	two	NUM
brj-23849	99	16	modules	module	NOUN
brj-23849	99	17	.	.	PUNCT
brj-23849	100	1	as	as	SCONJ
brj-23849	100	2	shown	show	VERB
brj-23849	100	3	in	in	ADP
brj-23849	100	4	fig	fig	NOUN
brj-23849	100	5	.	.	PUNCT
brj-23849	101	1	10	10	NUM
brj-23849	101	2	,	,	PUNCT
brj-23849	101	3	the	the	DET
brj-23849	101	4	loss	loss	NOUN
brj-23849	101	5	variation	variation	NOUN
brj-23849	101	6	of	of	ADP
brj-23849	101	7	the	the	DET
brj-23849	101	8	network	network	NOUN
brj-23849	101	9	when	when	SCONJ
brj-23849	101	10	bn	bn	PRON
brj-23849	101	11	layers	layer	NOUN
brj-23849	101	12	are	be	AUX
brj-23849	101	13	added	add	VERB
brj-23849	101	14	to	to	ADP
brj-23849	101	15	the	the	DET
brj-23849	101	16	first	first	ADJ
brj-23849	101	17	module	module	NOUN
brj-23849	101	18	,	,	PUNCT
brj-23849	101	19	second	second	ADJ
brj-23849	101	20	module	module	NOUN
brj-23849	101	21	,	,	PUNCT
brj-23849	101	22	third	third	ADJ
brj-23849	101	23	module	module	NOUN
brj-23849	101	24	,	,	PUNCT
brj-23849	101	25	and	and	CCONJ
brj-23849	101	26	fourth	fourth	ADJ
brj-23849	101	27	module	module	NOUN
brj-23849	101	28	of	of	ADP
brj-23849	101	29	the	the	DET
brj-23849	101	30	generator	generator	NOUN
brj-23849	101	31	network	network	NOUN
brj-23849	101	32	only	only	ADV
brj-23849	101	33	;	;	PUNCT
brj-23849	101	34	to	to	ADP
brj-23849	101	35	the	the	DET
brj-23849	101	36	first	first	ADJ
brj-23849	101	37	module	module	NOUN
brj-23849	101	38	,	,	PUNCT
brj-23849	101	39	second	second	ADJ
brj-23849	101	40	module	module	NOUN
brj-23849	101	41	,	,	PUNCT
brj-23849	101	42	third	third	ADJ
brj-23849	101	43	module	module	NOUN
brj-23849	101	44	,	,	PUNCT
brj-23849	101	45	and	and	CCONJ
brj-23849	101	46	fourth	fourth	ADJ
brj-23849	101	47	module	module	NOUN
brj-23849	101	48	of	of	ADP
brj-23849	101	49	the	the	DET
brj-23849	101	50	discriminator	discriminator	NOUN
brj-23849	101	51	network	network	NOUN
brj-23849	101	52	only	only	ADV
brj-23849	101	53	.	.	PUNCT
brj-23849	102	1	as	as	SCONJ
brj-23849	102	2	shown	show	VERB
brj-23849	102	3	in	in	ADP
brj-23849	102	4	fig	fig	NOUN
brj-23849	102	5	.	.	PUNCT
brj-23849	103	1	10	10	NUM
brj-23849	103	2	,	,	PUNCT
brj-23849	103	3	the	the	DET
brj-23849	103	4	loss	loss	NOUN
brj-23849	103	5	value	value	NOUN
brj-23849	103	6	change	change	NOUN
brj-23849	103	7	was	be	AUX
brj-23849	103	8	anomalous	anomalous	ADJ
brj-23849	103	9	when	when	SCONJ
brj-23849	103	10	adding	add	VERB
brj-23849	103	11	the	the	DET
brj-23849	103	12	bn	bn	ADJ
brj-23849	103	13	layer	layer	NOUN
brj-23849	103	14	to	to	ADP
brj-23849	103	15	the	the	DET
brj-23849	103	16	rest	rest	NOUN
brj-23849	103	17	of	of	ADP
brj-23849	103	18	the	the	DET
brj-23849	103	19	positions	position	NOUN
brj-23849	103	20	except	except	SCONJ
brj-23849	103	21	for	for	ADP
brj-23849	103	22	adding	add	VERB
brj-23849	103	23	the	the	DET
brj-23849	103	24	bn	bn	ADJ
brj-23849	103	25	layer	layer	NOUN
brj-23849	103	26	to	to	ADP
brj-23849	103	27	the	the	DET
brj-23849	103	28	second	second	ADJ
brj-23849	103	29	module	module	NOUN
brj-23849	103	30	and	and	CCONJ
brj-23849	103	31	the	the	DET
brj-23849	103	32	third	third	ADJ
brj-23849	103	33	module	module	NOUN
brj-23849	103	34	.	.	PUNCT
brj-23849	104	1	therefore	therefore	ADV
brj-23849	104	2	,	,	PUNCT
brj-23849	104	3	as	as	SCONJ
brj-23849	104	4	shown	show	VERB
brj-23849	104	5	in	in	ADP
brj-23849	104	6	table	table	NOUN
brj-23849	104	7	4	4	NUM
brj-23849	104	8	,	,	PUNCT
brj-23849	104	9	this	this	DET
brj-23849	104	10	work	work	NOUN
brj-23849	104	11	only	only	ADV
brj-23849	104	12	compares	compare	VERB
brj-23849	104	13	the	the	DET
brj-23849	104	14	fid	fid	NOUN
brj-23849	104	15	index	index	NOUN
brj-23849	104	16	scores	score	NOUN
brj-23849	104	17	of	of	ADP
brj-23849	104	18	the	the	DET
brj-23849	104	19	generated	generate	VERB
brj-23849	104	20	images	image	NOUN
brj-23849	104	21	when	when	SCONJ
brj-23849	104	22	the	the	DET
brj-23849	104	23	bn	bn	ADJ
brj-23849	104	24	layer	layer	NOUN
brj-23849	104	25	is	be	AUX
brj-23849	104	26	added	add	VERB
brj-23849	104	27	in	in	ADP
brj-23849	104	28	the	the	DET
brj-23849	104	29	second	second	ADJ
brj-23849	104	30	module	module	NOUN
brj-23849	104	31	,	,	PUNCT
brj-23849	104	32	the	the	DET
brj-23849	104	33	third	third	ADJ
brj-23849	104	34	module	module	NOUN
brj-23849	104	35	,	,	PUNCT
brj-23849	104	36	and	and	CCONJ
brj-23849	104	37	when	when	SCONJ
brj-23849	104	38	no	no	DET
brj-23849	104	39	bn	bn	ADJ
brj-23849	104	40	layer	layer	NOUN
brj-23849	104	41	is	be	AUX
brj-23849	104	42	added	add	VERB
brj-23849	104	43	.	.	PUNCT
brj-23849	105	1	peerreviewed	peerreviewe	VERB
brj-23849	105	2	article	article	NOUN
brj-23849	105	3	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	105	4	teng	teng	PROPN
brj-23849	105	5	et	et	PROPN
brj-23849	105	6	al	al	PROPN
brj-23849	105	7	.	.	PROPN
brj-23849	106	1	(	(	PUNCT
brj-23849	106	2	2024	2024	NUM
brj-23849	106	3	)	)	PUNCT
brj-23849	106	4	.	.	PUNCT
brj-23849	107	1	“	"	PUNCT
brj-23849	107	2	dcgan	dcgan	VERB
brj-23849	107	3	enhancement	enhancement	NOUN
brj-23849	107	4	method	method	NOUN
brj-23849	107	5	,	,	PUNCT
brj-23849	107	6	”	"	PUNCT
brj-23849	107	7	bioresources	bioresource	NOUN
brj-23849	107	8	19(4	19(4	NUM
brj-23849	107	9	)	)	PUNCT
brj-23849	107	10	,	,	PUNCT
brj-23849	107	11	9271	9271	NUM
brj-23849	107	12	-	-	SYM
brj-23849	107	13	9284	9284	NUM
brj-23849	107	14	.	.	PUNCT
brj-23849	108	1	9277	9277	NUM
brj-23849	108	2	(	(	PUNCT
brj-23849	108	3	a	a	X
brj-23849	108	4	)	)	PUNCT
brj-23849	108	5	bn	bn	NOUN
brj-23849	108	6	layer	layer	NOUN
brj-23849	108	7	added	add	VERB
brj-23849	108	8	to	to	ADP
brj-23849	108	9	the	the	DET
brj-23849	108	10	first	first	ADJ
brj-23849	108	11	module	module	NOUN
brj-23849	108	12	of	of	ADP
brj-23849	108	13	the	the	DET
brj-23849	108	14	generator	generator	NOUN
brj-23849	108	15	(	(	PUNCT
brj-23849	108	16	b	b	NOUN
brj-23849	108	17	)	)	PUNCT
brj-23849	108	18	bn	bn	NOUN
brj-23849	108	19	layer	layer	NOUN
brj-23849	108	20	added	add	VERB
brj-23849	108	21	to	to	ADP
brj-23849	108	22	the	the	DET
brj-23849	108	23	second	second	ADJ
brj-23849	108	24	module	module	NOUN
brj-23849	108	25	of	of	ADP
brj-23849	108	26	the	the	DET
brj-23849	108	27	generator	generator	NOUN
brj-23849	108	28	(	(	PUNCT
brj-23849	108	29	c	c	NOUN
brj-23849	108	30	)	)	PUNCT
brj-23849	108	31	add	add	VERB
brj-23849	108	32	bn	bn	ADP
brj-23849	108	33	layer	layer	NOUN
brj-23849	108	34	to	to	ADP
brj-23849	108	35	the	the	DET
brj-23849	108	36	third	third	ADJ
brj-23849	108	37	module	module	NOUN
brj-23849	108	38	of	of	ADP
brj-23849	108	39	the	the	DET
brj-23849	108	40	generator	generator	NOUN
brj-23849	108	41	(	(	PUNCT
brj-23849	108	42	d	d	X
brj-23849	108	43	)	)	PUNCT
brj-23849	108	44	add	add	VERB
brj-23849	108	45	bn	bn	ADP
brj-23849	108	46	layer	layer	NOUN
brj-23849	108	47	to	to	ADP
brj-23849	108	48	the	the	DET
brj-23849	108	49	4th	4th	ADJ
brj-23849	108	50	module	module	NOUN
brj-23849	108	51	of	of	ADP
brj-23849	108	52	the	the	DET
brj-23849	108	53	generator	generator	NOUN
brj-23849	108	54	(	(	PUNCT
brj-23849	108	55	e	e	NOUN
brj-23849	108	56	)	)	PUNCT
brj-23849	109	1	bn	bn	NOUN
brj-23849	109	2	layer	layer	NOUN
brj-23849	109	3	is	be	AUX
brj-23849	109	4	added	add	VERB
brj-23849	109	5	to	to	ADP
brj-23849	109	6	the	the	DET
brj-23849	109	7	first	first	ADJ
brj-23849	109	8	discriminator	discriminator	NOUN
brj-23849	109	9	module	module	NOUN
brj-23849	109	10	(	(	PUNCT
brj-23849	109	11	f	f	X
brj-23849	109	12	)	)	PUNCT
brj-23849	109	13	bn	bn	NOUN
brj-23849	109	14	layer	layer	NOUN
brj-23849	109	15	is	be	AUX
brj-23849	109	16	added	add	VERB
brj-23849	109	17	to	to	ADP
brj-23849	109	18	the	the	DET
brj-23849	109	19	second	second	ADJ
brj-23849	109	20	discriminator	discriminator	NOUN
brj-23849	109	21	module	module	NOUN
brj-23849	109	22	(	(	PUNCT
brj-23849	109	23	g	g	NOUN
brj-23849	109	24	)	)	PUNCT
brj-23849	109	25	bn	bn	NOUN
brj-23849	109	26	layer	layer	NOUN
brj-23849	109	27	is	be	AUX
brj-23849	109	28	added	add	VERB
brj-23849	109	29	to	to	ADP
brj-23849	109	30	the	the	DET
brj-23849	109	31	third	third	ADJ
brj-23849	109	32	discriminator	discriminator	NOUN
brj-23849	109	33	module	module	NOUN
brj-23849	109	34	(	(	PUNCT
brj-23849	109	35	h	h	NOUN
brj-23849	109	36	)	)	PUNCT
brj-23849	109	37	bn	bn	NOUN
brj-23849	109	38	layer	layer	NOUN
brj-23849	109	39	is	be	AUX
brj-23849	109	40	added	add	VERB
brj-23849	109	41	to	to	ADP
brj-23849	109	42	the	the	DET
brj-23849	109	43	fourth	fourth	ADJ
brj-23849	109	44	discriminator	discriminator	NOUN
brj-23849	109	45	module	module	NOUN
brj-23849	109	46	fig	fig	NOUN
brj-23849	109	47	.	.	PUNCT
brj-23849	110	1	10	10	NUM
brj-23849	110	2	.	.	PUNCT
brj-23849	111	1	variation	variation	NOUN
brj-23849	111	2	of	of	ADP
brj-23849	111	3	loss	loss	NOUN
brj-23849	111	4	values	value	NOUN
brj-23849	111	5	when	when	SCONJ
brj-23849	111	6	adding	add	VERB
brj-23849	111	7	bn	bn	ADP
brj-23849	111	8	layers	layer	NOUN
brj-23849	111	9	at	at	ADP
brj-23849	111	10	different	different	ADJ
brj-23849	111	11	locations	location	NOUN
brj-23849	111	12	table	table	NOUN
brj-23849	111	13	4	4	NUM
brj-23849	111	14	indicates	indicate	VERB
brj-23849	111	15	that	that	SCONJ
brj-23849	111	16	the	the	DET
brj-23849	111	17	generated	generate	VERB
brj-23849	111	18	image	image	NOUN
brj-23849	111	19	was	be	AUX
brj-23849	111	20	closest	close	ADJ
brj-23849	111	21	to	to	ADP
brj-23849	111	22	the	the	DET
brj-23849	111	23	real	real	ADJ
brj-23849	111	24	image	image	NOUN
brj-23849	111	25	when	when	SCONJ
brj-23849	111	26	bn	bn	PRON
brj-23849	111	27	layer	layer	NOUN
brj-23849	111	28	was	be	AUX
brj-23849	111	29	not	not	PART
brj-23849	111	30	used	use	VERB
brj-23849	111	31	.	.	PUNCT
brj-23849	112	1	figure	figure	VERB
brj-23849	112	2	11	11	NUM
brj-23849	112	3	shows	show	VERB
brj-23849	112	4	the	the	DET
brj-23849	112	5	images	image	NOUN
brj-23849	112	6	generated	generate	VERB
brj-23849	112	7	during	during	ADP
brj-23849	112	8	the	the	DET
brj-23849	112	9	training	training	NOUN
brj-23849	112	10	process	process	NOUN
brj-23849	112	11	for	for	ADP
brj-23849	112	12	the	the	DET
brj-23849	112	13	three	three	NUM
brj-23849	112	14	cases	case	NOUN
brj-23849	112	15	,	,	PUNCT
brj-23849	112	16	and	and	CCONJ
brj-23849	112	17	it	it	PRON
brj-23849	112	18	can	can	AUX
brj-23849	112	19	be	be	AUX
brj-23849	112	20	seen	see	VERB
brj-23849	112	21	that	that	SCONJ
brj-23849	112	22	the	the	DET
brj-23849	112	23	addition	addition	NOUN
brj-23849	112	24	of	of	ADP
brj-23849	112	25	the	the	DET
brj-23849	112	26	bn	bn	NOUN
brj-23849	112	27	layer	layer	NOUN
brj-23849	112	28	made	make	VERB
brj-23849	112	29	the	the	DET
brj-23849	112	30	network	network	NOUN
brj-23849	112	31	converge	converge	VERB
brj-23849	112	32	more	more	ADV
brj-23849	112	33	slowly	slowly	ADV
brj-23849	112	34	.	.	PUNCT
brj-23849	113	1	in	in	ADP
brj-23849	113	2	summary	summary	NOUN
brj-23849	113	3	,	,	PUNCT
brj-23849	113	4	the	the	DET
brj-23849	113	5	bn	bn	NOUN
brj-23849	113	6	layer	layer	NOUN
brj-23849	113	7	was	be	AUX
brj-23849	113	8	not	not	PART
brj-23849	113	9	used	use	VERB
brj-23849	113	10	in	in	ADP
brj-23849	113	11	this	this	DET
brj-23849	113	12	paper	paper	NOUN
brj-23849	113	13	.	.	PUNCT
brj-23849	114	1	gan	gin	VERB
brj-23849	114	2	-	-	PUNCT
brj-23849	114	3	generated	generate	VERB
brj-23849	114	4	image	image	NOUN
brj-23849	114	5	quality	quality	NOUN
brj-23849	114	6	evaluation	evaluation	NOUN
brj-23849	114	7	validation	validation	NOUN
brj-23849	114	8	is	be	AUX
brj-23849	114	9	uses	use	VERB
brj-23849	114	10	a	a	DET
brj-23849	114	11	pre	pre	ADJ
brj-23849	114	12	-	-	ADJ
brj-23849	114	13	trained	train	VERB
brj-23849	114	14	inception	inception	NOUN
brj-23849	114	15	network	network	NOUN
brj-23849	114	16	to	to	PART
brj-23849	114	17	classify	classify	VERB
brj-23849	114	18	the	the	DET
brj-23849	114	19	generated	generate	VERB
brj-23849	114	20	images	image	NOUN
brj-23849	114	21	and	and	CCONJ
brj-23849	114	22	evaluates	evaluate	VERB
brj-23849	114	23	how	how	SCONJ
brj-23849	114	24	confident	confident	ADJ
brj-23849	114	25	the	the	DET
brj-23849	114	26	network	network	NOUN
brj-23849	114	27	is	be	AUX
brj-23849	114	28	in	in	ADP
brj-23849	114	29	its	its	PRON
brj-23849	114	30	classifications	classification	NOUN
brj-23849	114	31	.	.	PUNCT
brj-23849	115	1	high	high	ADJ
brj-23849	115	2	confidence	confidence	NOUN
brj-23849	115	3	in	in	ADP
brj-23849	115	4	predictions	prediction	NOUN
brj-23849	115	5	(	(	PUNCT
brj-23849	115	6	low	low	ADJ
brj-23849	115	7	entropy	entropy	PROPN
brj-23849	115	8	)	)	PUNCT
brj-23849	115	9	and	and	CCONJ
brj-23849	115	10	a	a	DET
brj-23849	115	11	wide	wide	ADJ
brj-23849	115	12	variety	variety	NOUN
brj-23849	115	13	of	of	ADP
brj-23849	115	14	predicted	predict	VERB
brj-23849	115	15	classes	class	NOUN
brj-23849	115	16	contribute	contribute	VERB
brj-23849	115	17	to	to	ADP
brj-23849	115	18	a	a	DET
brj-23849	115	19	higher	high	ADJ
brj-23849	115	20	score	score	NOUN
brj-23849	115	21	.	.	PUNCT
brj-23849	116	1	however	however	ADV
brj-23849	116	2	,	,	PUNCT
brj-23849	116	3	is	be	AUX
brj-23849	116	4	has	have	VERB
brj-23849	116	5	limitations	limitation	NOUN
brj-23849	116	6	because	because	SCONJ
brj-23849	116	7	it	it	PRON
brj-23849	116	8	does	do	AUX
brj-23849	116	9	not	not	PART
brj-23849	116	10	directly	directly	ADV
brj-23849	116	11	compare	compare	VERB
brj-23849	116	12	generated	generate	VERB
brj-23849	116	13	images	image	NOUN
brj-23849	116	14	to	to	ADP
brj-23849	116	15	real	real	ADJ
brj-23849	116	16	ones	one	NOUN
brj-23849	116	17	and	and	CCONJ
brj-23849	116	18	may	may	AUX
brj-23849	116	19	give	give	VERB
brj-23849	116	20	high	high	ADJ
brj-23849	116	21	scores	score	NOUN
brj-23849	116	22	even	even	ADV
brj-23849	116	23	to	to	ADP
brj-23849	116	24	low	low	ADJ
brj-23849	116	25	-	-	PUNCT
brj-23849	116	26	quality	quality	NOUN
brj-23849	116	27	images	image	NOUN
brj-23849	116	28	if	if	SCONJ
brj-23849	116	29	they	they	PRON
brj-23849	116	30	appear	appear	VERB
brj-23849	116	31	diverse	diverse	ADJ
brj-23849	116	32	.	.	PUNCT
brj-23849	117	1	fid	fid	NOUN
brj-23849	117	2	compares	compare	VERB
brj-23849	117	3	the	the	DET
brj-23849	117	4	statistical	statistical	ADJ
brj-23849	117	5	distribution	distribution	NOUN
brj-23849	117	6	(	(	PUNCT
brj-23849	117	7	mean	mean	VERB
brj-23849	117	8	and	and	CCONJ
brj-23849	117	9	covariance	covariance	NOUN
brj-23849	117	10	)	)	PUNCT
brj-23849	117	11	of	of	ADP
brj-23849	117	12	generated	generate	VERB
brj-23849	117	13	images	image	NOUN
brj-23849	117	14	to	to	ADP
brj-23849	117	15	real	real	ADJ
brj-23849	117	16	images	image	NOUN
brj-23849	117	17	in	in	ADP
brj-23849	117	18	the	the	DET
brj-23849	117	19	feature	feature	NOUN
brj-23849	117	20	space	space	NOUN
brj-23849	117	21	of	of	ADP
brj-23849	117	22	a	a	DET
brj-23849	117	23	pre	pre	ADJ
brj-23849	117	24	-	-	ADJ
brj-23849	117	25	trained	train	VERB
brj-23849	117	26	network	network	NOUN
brj-23849	117	27	.	.	PUNCT
brj-23849	118	1	a	a	DET
brj-23849	118	2	lower	low	ADJ
brj-23849	118	3	fid	fid	NOUN
brj-23849	118	4	score	score	NOUN
brj-23849	118	5	means	mean	VERB
brj-23849	118	6	the	the	DET
brj-23849	118	7	generated	generate	VERB
brj-23849	118	8	images	image	NOUN
brj-23849	118	9	are	be	AUX
brj-23849	118	10	more	more	ADV
brj-23849	118	11	similar	similar	ADJ
brj-23849	118	12	to	to	ADP
brj-23849	118	13	real	real	ADJ
brj-23849	118	14	images	image	NOUN
brj-23849	118	15	in	in	ADP
brj-23849	118	16	terms	term	NOUN
brj-23849	118	17	of	of	ADP
brj-23849	118	18	quality	quality	NOUN
brj-23849	118	19	and	and	CCONJ
brj-23849	118	20	diversity	diversity	NOUN
brj-23849	118	21	.	.	PUNCT
brj-23849	119	1	fid	fid	NOUN
brj-23849	119	2	is	be	AUX
brj-23849	119	3	widely	widely	ADV
brj-23849	119	4	preferred	preferred	ADJ
brj-23849	119	5	because	because	SCONJ
brj-23849	119	6	it	it	PRON
brj-23849	119	7	provides	provide	VERB
brj-23849	119	8	a	a	DET
brj-23849	119	9	more	more	ADV
brj-23849	119	10	direct	direct	ADJ
brj-23849	119	11	and	and	CCONJ
brj-23849	119	12	reliable	reliable	ADJ
brj-23849	119	13	measure	measure	NOUN
brj-23849	119	14	of	of	ADP
brj-23849	119	15	image	image	NOUN
brj-23849	119	16	similarity	similarity	NOUN
brj-23849	119	17	and	and	CCONJ
brj-23849	119	18	quality	quality	NOUN
brj-23849	119	19	by	by	ADP
brj-23849	119	20	accounting	account	VERB
brj-23849	119	21	for	for	ADP
brj-23849	119	22	differences	difference	NOUN
brj-23849	119	23	in	in	ADP
brj-23849	119	24	the	the	DET
brj-23849	119	25	visual	visual	ADJ
brj-23849	119	26	features	feature	NOUN
brj-23849	119	27	of	of	ADP
brj-23849	119	28	both	both	DET
brj-23849	119	29	datasets	dataset	NOUN
brj-23849	119	30	.	.	PUNCT
brj-23849	120	1	peerreviewed	peerreviewe	VERB
brj-23849	120	2	article	article	NOUN
brj-23849	120	3	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	120	4	teng	teng	PROPN
brj-23849	120	5	et	et	PROPN
brj-23849	120	6	al	al	PROPN
brj-23849	120	7	.	.	PROPN
brj-23849	121	1	(	(	PUNCT
brj-23849	121	2	2024	2024	NUM
brj-23849	121	3	)	)	PUNCT
brj-23849	121	4	.	.	PUNCT
brj-23849	122	1	“	"	PUNCT
brj-23849	122	2	dcgan	dcgan	VERB
brj-23849	122	3	enhancement	enhancement	NOUN
brj-23849	122	4	method	method	NOUN
brj-23849	122	5	,	,	PUNCT
brj-23849	122	6	”	"	PUNCT
brj-23849	122	7	bioresources	bioresource	NOUN
brj-23849	122	8	19(4	19(4	NUM
brj-23849	122	9	)	)	PUNCT
brj-23849	122	10	,	,	PUNCT
brj-23849	122	11	9271	9271	NUM
brj-23849	122	12	-	-	SYM
brj-23849	122	13	9284	9284	NUM
brj-23849	122	14	.	.	PUNCT
brj-23849	123	1	9278	9278	NUM
brj-23849	123	2	table	table	NOUN
brj-23849	123	3	4	4	NUM
brj-23849	123	4	.	.	NOUN
brj-23849	123	5	fid	fid	NOUN
brj-23849	123	6	scores	score	NOUN
brj-23849	123	7	or	or	CCONJ
brj-23849	123	8	different	different	ADJ
brj-23849	123	9	cases	case	NOUN
brj-23849	123	10	of	of	ADP
brj-23849	123	11	adding	add	VERB
brj-23849	123	12	bn	bn	NUM
brj-23849	123	13	layers	layer	NOUN
brj-23849	123	14	location	location	NOUN
brj-23849	123	15	of	of	ADP
brj-23849	123	16	the	the	DET
brj-23849	123	17	added	add	VERB
brj-23849	123	18	bn	bn	NOUN
brj-23849	123	19	layer	layer	NOUN
brj-23849	123	20	fid	fid	NOUN
brj-23849	123	21	score	score	NOUN
brj-23849	123	22	add	add	VERB
brj-23849	123	23	the	the	DET
brj-23849	123	24	bn	bn	NOUN
brj-23849	123	25	layer	layer	NOUN
brj-23849	123	26	to	to	ADP
brj-23849	123	27	the	the	DET
brj-23849	123	28	second	second	ADJ
brj-23849	123	29	module	module	NOUN
brj-23849	123	30	278.06	278.06	NUM
brj-23849	123	31	add	add	VERB
brj-23849	123	32	the	the	DET
brj-23849	123	33	bn	bn	NOUN
brj-23849	123	34	layer	layer	NOUN
brj-23849	123	35	to	to	ADP
brj-23849	123	36	the	the	DET
brj-23849	123	37	third	third	ADJ
brj-23849	123	38	module	module	NOUN
brj-23849	123	39	247.97	247.97	NUM
brj-23849	123	40	no	no	PRON
brj-23849	123	41	bn	bn	NOUN
brj-23849	123	42	layer	layer	NOUN
brj-23849	123	43	is	be	AUX
brj-23849	123	44	used	use	VERB
brj-23849	123	45	147.29	147.29	NUM
brj-23849	123	46	fig	fig	NOUN
brj-23849	123	47	.	.	PUNCT
brj-23849	124	1	11	11	NUM
brj-23849	124	2	.	.	PUNCT
brj-23849	125	1	the	the	DET
brj-23849	125	2	training	training	NOUN
brj-23849	125	3	process	process	NOUN
brj-23849	125	4	for	for	ADP
brj-23849	125	5	different	different	ADJ
brj-23849	125	6	cases	case	NOUN
brj-23849	125	7	of	of	ADP
brj-23849	125	8	adding	add	VERB
brj-23849	125	9	bn	bn	ADP
brj-23849	125	10	layers	layer	NOUN
brj-23849	125	11	the	the	DET
brj-23849	125	12	gan	gan	PROPN
brj-23849	125	13	has	have	VERB
brj-23849	125	14	two	two	NUM
brj-23849	125	15	evaluation	evaluation	NOUN
brj-23849	125	16	metrics	metric	NOUN
brj-23849	125	17	:	:	PUNCT
brj-23849	125	18	the	the	PRON
brj-23849	125	19	is	be	AUX
brj-23849	125	20	metric	metric	ADJ
brj-23849	125	21	and	and	CCONJ
brj-23849	125	22	the	the	DET
brj-23849	125	23	fid	fid	NOUN
brj-23849	125	24	metric	metric	NOUN
brj-23849	125	25	.	.	PUNCT
brj-23849	126	1	the	the	PRON
brj-23849	126	2	is	be	AUX
brj-23849	126	3	metric	metric	ADJ
brj-23849	126	4	scores	score	NOUN
brj-23849	126	5	a	a	DET
brj-23849	126	6	single	single	ADJ
brj-23849	126	7	dataset	dataset	NOUN
brj-23849	126	8	by	by	ADP
brj-23849	126	9	comparing	compare	VERB
brj-23849	126	10	it	it	PRON
brj-23849	126	11	to	to	ADP
brj-23849	126	12	a	a	DET
brj-23849	126	13	single	single	ADJ
brj-23849	126	14	dataset	dataset	NOUN
brj-23849	126	15	,	,	PUNCT
brj-23849	126	16	with	with	ADP
brj-23849	126	17	larger	large	ADJ
brj-23849	126	18	values	value	NOUN
brj-23849	126	19	indicating	indicate	VERB
brj-23849	126	20	higher	high	ADJ
brj-23849	126	21	image	image	NOUN
brj-23849	126	22	quality	quality	NOUN
brj-23849	126	23	and	and	CCONJ
brj-23849	126	24	category	category	NOUN
brj-23849	126	25	richness	richness	NOUN
brj-23849	126	26	.	.	PUNCT
brj-23849	127	1	the	the	DET
brj-23849	127	2	fid	fid	NOUN
brj-23849	127	3	gives	give	VERB
brj-23849	127	4	a	a	DET
brj-23849	127	5	score	score	NOUN
brj-23849	127	6	by	by	ADP
brj-23849	127	7	comparing	compare	VERB
brj-23849	127	8	the	the	DET
brj-23849	127	9	real	real	ADJ
brj-23849	127	10	image	image	NOUN
brj-23849	127	11	to	to	ADP
brj-23849	127	12	the	the	DET
brj-23849	127	13	generated	generate	VERB
brj-23849	127	14	image	image	NOUN
brj-23849	127	15	,	,	PUNCT
brj-23849	127	16	with	with	ADP
brj-23849	127	17	lower	low	ADJ
brj-23849	127	18	scores	score	NOUN
brj-23849	127	19	indicating	indicate	VERB
brj-23849	127	20	that	that	SCONJ
brj-23849	127	21	the	the	DET
brj-23849	127	22	generated	generate	VERB
brj-23849	127	23	image	image	NOUN
brj-23849	127	24	is	be	AUX
brj-23849	127	25	closer	close	ADJ
brj-23849	127	26	to	to	ADP
brj-23849	127	27	the	the	DET
brj-23849	127	28	real	real	ADJ
brj-23849	127	29	image	image	NOUN
brj-23849	127	30	.	.	PUNCT
brj-23849	128	1	the	the	DET
brj-23849	128	2	specificity	specificity	NOUN
brj-23849	128	3	of	of	ADP
brj-23849	128	4	the	the	DET
brj-23849	128	5	american	american	PROPN
brj-23849	128	6	hyphantria	hyphantria	PROPN
brj-23849	128	7	cunea	cunea	PROPN
brj-23849	128	8	larvae	larvae	PROPN
brj-23849	128	9	net	net	ADJ
brj-23849	128	10	curtain	curtain	NOUN
brj-23849	128	11	images	image	NOUN
brj-23849	128	12	is	be	AUX
brj-23849	128	13	illustrated	illustrate	VERB
brj-23849	128	14	in	in	ADP
brj-23849	128	15	fig	fig	NOUN
brj-23849	128	16	.	.	PUNCT
brj-23849	129	1	6	6	NUM
brj-23849	129	2	.	.	PUNCT
brj-23849	130	1	the	the	DET
brj-23849	130	2	images	image	NOUN
brj-23849	130	3	shown	show	VERB
brj-23849	130	4	in	in	ADP
brj-23849	130	5	fig	fig	NOUN
brj-23849	130	6	.	.	PUNCT
brj-23849	131	1	6	6	NUM
brj-23849	131	2	belong	belong	VERB
brj-23849	131	3	to	to	ADP
brj-23849	131	4	a	a	DET
brj-23849	131	5	certain	certain	ADJ
brj-23849	131	6	class	class	NOUN
brj-23849	131	7	of	of	ADP
brj-23849	131	8	training	training	NOUN
brj-23849	131	9	set	set	NOUN
brj-23849	131	10	,	,	PUNCT
brj-23849	131	11	in	in	ADP
brj-23849	131	12	which	which	PRON
brj-23849	131	13	the	the	DET
brj-23849	131	14	images	image	NOUN
brj-23849	131	15	are	be	AUX
brj-23849	131	16	relatively	relatively	ADV
brj-23849	131	17	very	very	ADV
brj-23849	131	18	similar	similar	ADJ
brj-23849	131	19	.	.	PUNCT
brj-23849	132	1	but	but	CCONJ
brj-23849	132	2	even	even	ADV
brj-23849	132	3	so	so	ADV
brj-23849	132	4	,	,	PUNCT
brj-23849	132	5	when	when	SCONJ
brj-23849	132	6	the	the	DET
brj-23849	132	7	images	image	NOUN
brj-23849	132	8	in	in	ADP
brj-23849	132	9	the	the	DET
brj-23849	132	10	training	training	NOUN
brj-23849	132	11	set	set	NOUN
brj-23849	132	12	shown	show	VERB
brj-23849	132	13	in	in	ADP
brj-23849	132	14	fig	fig	NOUN
brj-23849	132	15	.	.	PUNCT
brj-23849	133	1	1	1	NUM
brj-23849	133	2	were	be	AUX
brj-23849	133	3	divided	divide	VERB
brj-23849	133	4	into	into	ADP
brj-23849	133	5	two	two	NUM
brj-23849	133	6	parts	part	NOUN
brj-23849	133	7	and	and	CCONJ
brj-23849	133	8	their	their	PRON
brj-23849	133	9	fid	fid	NOUN
brj-23849	133	10	scores	score	NOUN
brj-23849	133	11	were	be	AUX
brj-23849	133	12	evaluated	evaluate	VERB
brj-23849	133	13	,	,	PUNCT
brj-23849	133	14	the	the	DET
brj-23849	133	15	fid	fid	NOUN
brj-23849	133	16	scores	score	NOUN
brj-23849	133	17	were	be	AUX
brj-23849	133	18	still	still	ADV
brj-23849	133	19	as	as	ADV
brj-23849	133	20	high	high	ADJ
brj-23849	133	21	as	as	ADP
brj-23849	133	22	122	122	NUM
brj-23849	133	23	.	.	PUNCT
brj-23849	134	1	to	to	PART
brj-23849	134	2	explore	explore	VERB
brj-23849	134	3	further	far	ADV
brj-23849	134	4	,	,	PUNCT
brj-23849	134	5	the	the	DET
brj-23849	134	6	paper	paper	NOUN
brj-23849	134	7	used	use	VERB
brj-23849	134	8	the	the	DET
brj-23849	134	9	same	same	ADJ
brj-23849	134	10	method	method	NOUN
brj-23849	134	11	described	describe	VERB
brj-23849	134	12	above	above	ADV
brj-23849	134	13	for	for	ADP
brj-23849	134	14	other	other	ADJ
brj-23849	134	15	category	category	NOUN
brj-23849	134	16	datasets	dataset	NOUN
brj-23849	134	17	and	and	CCONJ
brj-23849	134	18	evaluated	evaluate	VERB
brj-23849	134	19	their	their	PRON
brj-23849	134	20	fid	fid	NOUN
brj-23849	134	21	scores	score	NOUN
brj-23849	134	22	.	.	PUNCT
brj-23849	135	1	it	it	PRON
brj-23849	135	2	was	be	AUX
brj-23849	135	3	finally	finally	ADV
brj-23849	135	4	found	find	VERB
brj-23849	135	5	that	that	SCONJ
brj-23849	135	6	the	the	DET
brj-23849	135	7	fid	fid	NOUN
brj-23849	135	8	scores	score	NOUN
brj-23849	135	9	between	between	ADP
brj-23849	135	10	different	different	ADJ
brj-23849	135	11	images	image	NOUN
brj-23849	135	12	in	in	ADP
brj-23849	135	13	the	the	DET
brj-23849	135	14	training	training	NOUN
brj-23849	135	15	set	set	NOUN
brj-23849	135	16	of	of	ADP
brj-23849	135	17	the	the	DET
brj-23849	135	18	same	same	ADJ
brj-23849	135	19	category	category	NOUN
brj-23849	135	20	ranged	range	VERB
brj-23849	135	21	from	from	ADP
brj-23849	135	22	100	100	NUM
brj-23849	135	23	to	to	ADP
brj-23849	135	24	350	350	NUM
brj-23849	135	25	.	.	PUNCT
brj-23849	136	1	fig	fig	NOUN
brj-23849	136	2	.	.	PUNCT
brj-23849	137	1	12	12	NUM
brj-23849	137	2	.	.	PUNCT
brj-23849	137	3	images	image	NOUN
brj-23849	137	4	contained	contain	VERB
brj-23849	137	5	in	in	ADP
brj-23849	137	6	a	a	DET
brj-23849	137	7	certain	certain	ADJ
brj-23849	137	8	type	type	NOUN
brj-23849	137	9	of	of	ADP
brj-23849	137	10	training	training	NOUN
brj-23849	137	11	set	set	VERB
brj-23849	137	12	improved	improve	VERB
brj-23849	137	13	generative	generative	ADJ
brj-23849	137	14	and	and	CCONJ
brj-23849	137	15	discriminative	discriminative	NOUN
brj-23849	137	16	networks	network	NOUN
brj-23849	137	17	leakyrelu	leakyrelu	NOUN
brj-23849	137	18	was	be	AUX
brj-23849	137	19	used	use	VERB
brj-23849	137	20	as	as	ADP
brj-23849	137	21	the	the	DET
brj-23849	137	22	activation	activation	NOUN
brj-23849	137	23	function	function	NOUN
brj-23849	137	24	at	at	ADP
brj-23849	137	25	the	the	DET
brj-23849	137	26	end	end	NOUN
brj-23849	137	27	of	of	ADP
brj-23849	137	28	each	each	DET
brj-23849	137	29	resize	resize	NOUN
brj-23849	137	30	convolution	convolution	NOUN
brj-23849	137	31	layer	layer	NOUN
brj-23849	137	32	in	in	ADP
brj-23849	137	33	the	the	DET
brj-23849	137	34	hidden	hide	VERB
brj-23849	137	35	layer	layer	NOUN
brj-23849	137	36	.	.	PUNCT
brj-23849	138	1	the	the	DET
brj-23849	138	2	structure	structure	NOUN
brj-23849	138	3	of	of	ADP
brj-23849	138	4	the	the	DET
brj-23849	138	5	generator	generator	NOUN
brj-23849	138	6	network	network	NOUN
brj-23849	138	7	is	be	AUX
brj-23849	138	8	shown	show	VERB
brj-23849	138	9	in	in	ADP
brj-23849	138	10	epoch	epoch	PROPN
brj-23849	138	11	0	0	NUM
brj-23849	138	12	400	400	NUM
brj-23849	138	13	1000	1000	NUM
brj-23849	138	14	2000	2000	NUM
brj-23849	138	15	3000	3000	NUM
brj-23849	138	16	4000	4000	NUM
brj-23849	138	17	5000	5000	NUM
brj-23849	138	18	(	(	PUNCT
brj-23849	138	19	a	a	NOUN
brj-23849	138	20	)	)	PUNCT
brj-23849	138	21	bn	bn	NOUN
brj-23849	138	22	layer	layer	NOUN
brj-23849	138	23	added	add	VERB
brj-23849	138	24	to	to	ADP
brj-23849	138	25	the	the	DET
brj-23849	138	26	second	second	ADJ
brj-23849	138	27	module	module	NOUN
brj-23849	138	28	epoch	epoch	NOUN
brj-23849	138	29	0	0	NUM
brj-23849	138	30	400	400	NUM
brj-23849	138	31	1000	1000	NUM
brj-23849	138	32	2000	2000	NUM
brj-23849	138	33	3000	3000	NUM
brj-23849	138	34	4000	4000	NUM
brj-23849	138	35	5000	5000	NUM
brj-23849	138	36	(	(	PUNCT
brj-23849	138	37	b	b	NOUN
brj-23849	138	38	)	)	PUNCT
brj-23849	138	39	bn	bn	NOUN
brj-23849	139	1	layer	layer	NOUN
brj-23849	139	2	added	add	VERB
brj-23849	139	3	to	to	ADP
brj-23849	139	4	the	the	DET
brj-23849	139	5	third	third	ADJ
brj-23849	139	6	module	module	NOUN
brj-23849	139	7	epoch	epoch	NOUN
brj-23849	139	8	0	0	NUM
brj-23849	139	9	400	400	NUM
brj-23849	139	10	1000	1000	NUM
brj-23849	139	11	2000	2000	NUM
brj-23849	139	12	3000	3000	NUM
brj-23849	139	13	4000	4000	NUM
brj-23849	139	14	5000	5000	NUM
brj-23849	139	15	(	(	PUNCT
brj-23849	139	16	c	c	NOUN
brj-23849	139	17	)	)	PUNCT
brj-23849	139	18	no	no	PRON
brj-23849	139	19	bn	bn	NOUN
brj-23849	139	20	layer	layer	NOUN
brj-23849	139	21	is	be	AUX
brj-23849	139	22	used	use	VERB
brj-23849	139	23	peerreviewed	peerreviewed	ADJ
brj-23849	139	24	article	article	NOUN
brj-23849	139	25	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23849	139	26	teng	teng	PROPN
brj-23849	139	27	et	et	PROPN
brj-23849	139	28	al	al	PROPN
brj-23849	139	29	.	.	PROPN
brj-23849	140	1	(	(	PUNCT
brj-23849	140	2	2024	2024	NUM
brj-23849	140	3	)	)	PUNCT
brj-23849	140	4	.	.	PUNCT
brj-23849	141	1	“	"	PUNCT
brj-23849	141	2	dcgan	dcgan	VERB
brj-23849	141	3	enhancement	enhancement	NOUN
brj-23849	141	4	method	method	NOUN
brj-23849	141	5	,	,	PUNCT
brj-23849	141	6	”	"	PUNCT
brj-23849	141	7	bioresources	bioresource	NOUN
brj-23849	141	8	19(4	19(4	NUM
brj-23849	141	9	)	)	PUNCT
brj-23849	141	10	,	,	PUNCT
brj-23849	141	11	9271	9271	NUM
brj-23849	141	12	-	-	SYM
brj-23849	141	13	9284	9284	NUM
brj-23849	141	14	.	.	PUNCT
brj-23849	142	1	9279	9279	NUM
brj-23849	142	2	fig	fig	NOUN
brj-23849	142	3	.	.	PUNCT
brj-23849	143	1	13(a	13(a	NUM
brj-23849	143	2	)	)	PUNCT
brj-23849	143	3	,	,	PUNCT
brj-23849	143	4	and	and	CCONJ
brj-23849	143	5	the	the	DET
brj-23849	143	6	structure	structure	NOUN
brj-23849	143	7	of	of	ADP
brj-23849	143	8	the	the	DET
brj-23849	143	9	discriminator	discriminator	NOUN
brj-23849	143	10	network	network	NOUN
brj-23849	143	11	is	be	AUX
brj-23849	143	12	shown	show	VERB
brj-23849	143	13	in	in	ADP
brj-23849	143	14	fig	fig	NOUN
brj-23849	143	15	.	.	PUNCT
brj-23849	144	1	13(b	13(b	NUM
brj-23849	144	2	)	)	PUNCT
brj-23849	144	3	.	.	PUNCT
brj-23849	145	1	the	the	DET
brj-23849	145	2	architecture	architecture	NOUN
brj-23849	145	3	of	of	ADP
brj-23849	145	4	the	the	DET
brj-23849	145	5	generator	generator	NOUN
brj-23849	145	6	and	and	CCONJ
brj-23849	145	7	discriminator	discriminator	NOUN
brj-23849	145	8	are	be	AUX
brj-23849	145	9	shown	show	VERB
brj-23849	145	10	in	in	ADP
brj-23849	145	11	fig	fig	NOUN
brj-23849	145	12	.	.	PUNCT
brj-23849	146	1	14(a	14(a	NUM
brj-23849	146	2	)	)	PUNCT
brj-23849	146	3	and	and	CCONJ
brj-23849	146	4	fig	fig	NOUN
brj-23849	146	5	.	.	PUNCT
brj-23849	147	1	14(b	14(b	NUM
brj-23849	147	2	)	)	PUNCT
brj-23849	147	3	,	,	PUNCT
brj-23849	147	4	respectively	respectively	ADV
brj-23849	147	5	.	.	PUNCT
brj-23849	148	1	(	(	PUNCT
brj-23849	148	2	a	a	X
brj-23849	148	3	)	)	PUNCT
brj-23849	148	4	generator	generator	NOUN
brj-23849	148	5	network	network	NOUN
brj-23849	148	6	structure	structure	NOUN
brj-23849	148	7	(	(	PUNCT
brj-23849	148	8	b	b	NOUN
brj-23849	148	9	)	)	PUNCT
brj-23849	148	10	discriminator	discriminator	NOUN
brj-23849	148	11	network	network	NOUN
brj-23849	148	12	structure	structure	NOUN
brj-23849	148	13	fig	fig	NOUN
brj-23849	148	14	.	.	PUNCT
brj-23849	149	1	13	13	NUM
brj-23849	149	2	.	.	X
brj-23849	149	3	generator	generator	NOUN
brj-23849	149	4	network	network	NOUN
brj-23849	149	5	structure	structure	NOUN
brj-23849	149	6	(	(	PUNCT
brj-23849	149	7	a	a	X
brj-23849	149	8	)	)	PUNCT
brj-23849	149	9	the	the	DET
brj-23849	149	10	architecture	architecture	NOUN
brj-23849	149	11	of	of	ADP
brj-23849	149	12	generators	generator	NOUN
brj-23849	149	13	generator	generator	NOUN
brj-23849	149	14	network	network	NOUN
brj-23849	149	15	(	(	PUNCT
brj-23849	149	16	b	b	NOUN
brj-23849	149	17	)	)	PUNCT
brj-23849	149	18	the	the	DET
brj-23849	149	19	architecture	architecture	NOUN
brj-23849	149	20	of	of	ADP
brj-23849	149	21	the	the	DET
brj-23849	149	22	discriminator	discriminator	NOUN
brj-23849	149	23	network	network	NOUN
brj-23849	149	24	fig	fig	NOUN
brj-23849	149	25	.	.	PUNCT
brj-23849	150	1	14	14	NUM
brj-23849	150	2	.	.	PUNCT
brj-23849	151	1	architecture	architecture	NOUN
brj-23849	151	2	of	of	ADP
brj-23849	151	3	the	the	DET
brj-23849	151	4	generator	generator	NOUN
brj-23849	151	5	and	and	CCONJ
brj-23849	151	6	discriminator	discriminator	NOUN
brj-23849	151	7	experimental	experimental	ADJ
brj-23849	151	8	platform	platform	NOUN
brj-23849	151	9	and	and	CCONJ
brj-23849	151	10	parameter	parameter	NOUN
brj-23849	151	11	settings	setting	NOUN
brj-23849	151	12	the	the	DET
brj-23849	151	13	computer	computer	NOUN
brj-23849	151	14	operating	operate	VERB
brj-23849	151	15	system	system	NOUN
brj-23849	151	16	used	use	VERB
brj-23849	151	17	in	in	ADP
brj-23849	151	18	this	this	DET
brj-23849	151	19	paper	paper	NOUN
brj-23849	151	20	was	be	AUX
brj-23849	151	21	windows	window	NOUN
brj-23849	151	22	64bit	64bit	NOUN
brj-23849	151	23	system	system	NOUN
brj-23849	151	24	,	,	PUNCT
brj-23849	151	25	and	and	CCONJ
brj-23849	151	26	the	the	DET
brj-23849	151	27	hardware	hardware	NOUN
brj-23849	151	28	used	use	VERB
brj-23849	151	29	in	in	ADP
brj-23849	151	30	this	this	DET
brj-23849	151	31	experiment	experiment	NOUN
brj-23849	151	32	was	be	AUX
brj-23849	151	33	intel(r	intel(r	NOUN
brj-23849	151	34	)	)	PUNCT
brj-23849	151	35	core(tm	core(tm	NOUN
brj-23849	151	36	)	)	PUNCT
brj-23849	151	37	i76700	i76700	PROPN
brj-23849	151	38	cpu	cpu	NOUN
brj-23849	151	39	,	,	PUNCT
brj-23849	151	40	16	16	NUM
brj-23849	151	41	gb	gb	NOUN
brj-23849	151	42	ram	ram	NOUN
brj-23849	151	43	,	,	PUNCT
brj-23849	151	44	and	and	CCONJ
brj-23849	151	45	nvidia	nvidia	PROPN
brj-23849	151	46	geforce	geforce	NOUN
brj-23849	151	47	rtx	rtx	PROPN
brj-23849	151	48	2080	2080	NUM
brj-23849	151	49	ti	ti	NOUN
brj-23849	151	50	14	14	NUM
brj-23849	151	51	gb	gb	NOUN
brj-23849	151	52	,	,	PUNCT
brj-23849	151	53	and	and	CCONJ
brj-23849	151	54	the	the	DET
brj-23849	151	55	software	software	NOUN
brj-23849	151	56	environment	environment	NOUN
brj-23849	151	57	is	be	AUX
brj-23849	151	58	tensorflow	tensorflow	ADJ
brj-23849	151	59	gpu	gpu	PROPN
brj-23849	151	60	2.0.0	2.0.0	NUM
brj-23849	151	61	(	(	PUNCT
brj-23849	151	62	google	google	PROPN
brj-23849	151	63	llc	llc	PROPN
brj-23849	151	64	;	;	PUNCT
brj-23849	151	65	mountain	mountain	NOUN
brj-23849	151	66	view	view	NOUN
brj-23849	151	67	,	,	PUNCT
brj-23849	151	68	ca	ca	PROPN
brj-23849	151	69	,	,	PUNCT
brj-23849	151	70	usa	usa	PROPN
brj-23849	151	71	)	)	PUNCT
brj-23849	151	72	and	and	CCONJ
brj-23849	151	73	keras	keras	PROPN
brj-23849	151	74	2.3.1	2.3.1	PROPN
brj-23849	151	75	(	(	PUNCT
brj-23849	151	76	google	google	PROPN
brj-23849	151	77	llc	llc	PROPN
brj-23849	151	78	;	;	PUNCT
brj-23849	151	79	mountain	mountain	NOUN
brj-23849	151	80	view	view	NOUN
brj-23849	151	81	,	,	PUNCT
brj-23849	151	82	ca	ca	PROPN
brj-23849	151	83	,	,	PUNCT
brj-23849	151	84	usa	usa	PROPN
brj-23849	151	85	)	)	PUNCT
brj-23849	151	86	.	.	PUNCT
brj-23849	152	1	the	the	DET
brj-23849	152	2	batch	batch	NOUN
brj-23849	152	3	size	size	NOUN
brj-23849	152	4	of	of	ADP
brj-23849	152	5	the	the	DET
brj-23849	152	6	training	training	NOUN
brj-23849	152	7	process	process	NOUN
brj-23849	152	8	was	be	AUX
brj-23849	152	9	set	set	VERB
brj-23849	152	10	to	to	ADP
brj-23849	152	11	64	64	NUM
brj-23849	152	12	,	,	PUNCT
brj-23849	152	13	and	and	CCONJ
brj-23849	152	14	the	the	DET
brj-23849	152	15	peerreviewed	peerreviewe	VERB
brj-23849	152	16	article	article	NOUN
brj-23849	152	17	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	152	18	teng	teng	PROPN
brj-23849	152	19	et	et	PROPN
brj-23849	152	20	al	al	PROPN
brj-23849	152	21	.	.	PROPN
brj-23849	153	1	(	(	PUNCT
brj-23849	153	2	2024	2024	NUM
brj-23849	153	3	)	)	PUNCT
brj-23849	153	4	.	.	PUNCT
brj-23849	154	1	“	"	PUNCT
brj-23849	154	2	dcgan	dcgan	VERB
brj-23849	154	3	enhancement	enhancement	NOUN
brj-23849	154	4	method	method	NOUN
brj-23849	154	5	,	,	PUNCT
brj-23849	154	6	”	"	PUNCT
brj-23849	154	7	bioresources	bioresource	NOUN
brj-23849	154	8	19(4	19(4	NUM
brj-23849	154	9	)	)	PUNCT
brj-23849	154	10	,	,	PUNCT
brj-23849	154	11	9271	9271	NUM
brj-23849	154	12	-	-	SYM
brj-23849	154	13	9284	9284	NUM
brj-23849	154	14	.	.	PUNCT
brj-23849	155	1	9280	9280	NUM
brj-23849	155	2	leakyrelu	leakyrelu	ADJ
brj-23849	155	3	slope	slope	NOUN
brj-23849	155	4	parameter	parameter	NOUN
brj-23849	155	5	was	be	AUX
brj-23849	155	6	set	set	VERB
brj-23849	155	7	to	to	ADP
brj-23849	155	8	0.2	0.2	NUM
brj-23849	155	9	.	.	PUNCT
brj-23849	156	1	the	the	DET
brj-23849	156	2	learning	learning	NOUN
brj-23849	156	3	rate	rate	NOUN
brj-23849	156	4	of	of	ADP
brj-23849	156	5	the	the	DET
brj-23849	156	6	optimizer	optimizer	NOUN
brj-23849	156	7	adam	adam	PROPN
brj-23849	156	8	was	be	AUX
brj-23849	156	9	set	set	VERB
brj-23849	156	10	to	to	ADP
brj-23849	156	11	1e05	1e05	NUM
brj-23849	156	12	,	,	PUNCT
brj-23849	156	13	the	the	DET
brj-23849	156	14	parameter	parameter	NOUN
brj-23849	156	15	beta_1	beta_1	NOUN
brj-23849	156	16	was	be	AUX
brj-23849	156	17	set	set	VERB
brj-23849	156	18	to	to	ADP
brj-23849	156	19	0.5	0.5	NUM
brj-23849	156	20	,	,	PUNCT
brj-23849	156	21	and	and	CCONJ
brj-23849	156	22	epsilon	epsilon	PROPN
brj-23849	156	23	was	be	AUX
brj-23849	156	24	set	set	VERB
brj-23849	156	25	to	to	ADP
brj-23849	156	26	1e05	1e05	NUM
brj-23849	156	27	.	.	PUNCT
brj-23849	157	1	results	result	NOUN
brj-23849	157	2	and	and	CCONJ
brj-23849	157	3	discussion	discussion	VERB
brj-23849	157	4	the	the	DET
brj-23849	157	5	training	training	NOUN
brj-23849	157	6	process	process	NOUN
brj-23849	157	7	is	be	AUX
brj-23849	157	8	shown	show	VERB
brj-23849	157	9	in	in	ADP
brj-23849	157	10	fig	fig	NOUN
brj-23849	157	11	.	.	PUNCT
brj-23849	158	1	15	15	NUM
brj-23849	158	2	.	.	PUNCT
brj-23849	159	1	the	the	DET
brj-23849	159	2	comparison	comparison	NOUN
brj-23849	159	3	between	between	ADP
brj-23849	159	4	the	the	DET
brj-23849	159	5	generated	generate	VERB
brj-23849	159	6	image	image	NOUN
brj-23849	159	7	and	and	CCONJ
brj-23849	159	8	the	the	DET
brj-23849	159	9	original	original	ADJ
brj-23849	159	10	image	image	NOUN
brj-23849	159	11	is	be	AUX
brj-23849	159	12	shown	show	VERB
brj-23849	159	13	in	in	ADP
brj-23849	159	14	fig	fig	NOUN
brj-23849	159	15	.	.	PUNCT
brj-23849	160	1	16	16	NUM
brj-23849	160	2	.	.	PUNCT
brj-23849	161	1	epoch	epoch	PROPN
brj-23849	161	2	0	0	NUM
brj-23849	161	3	100	100	NUM
brj-23849	161	4	1000	1000	NUM
brj-23849	161	5	2000	2000	NUM
brj-23849	161	6	3000	3000	NUM
brj-23849	161	7	4000	4000	NUM
brj-23849	161	8	5000	5000	NUM
brj-23849	161	9	fig	fig	NOUN
brj-23849	161	10	.	.	PUNCT
brj-23849	162	1	15	15	NUM
brj-23849	162	2	.	.	PUNCT
brj-23849	163	1	gan	gan	ADJ
brj-23849	163	2	training	training	NOUN
brj-23849	163	3	process	process	NOUN
brj-23849	163	4	peerreviewed	peerreviewe	VERB
brj-23849	163	5	article	article	NOUN
brj-23849	163	6	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	163	7	teng	teng	PROPN
brj-23849	163	8	et	et	PROPN
brj-23849	163	9	al	al	PROPN
brj-23849	163	10	.	.	PROPN
brj-23849	164	1	(	(	PUNCT
brj-23849	164	2	2024	2024	NUM
brj-23849	164	3	)	)	PUNCT
brj-23849	164	4	.	.	PUNCT
brj-23849	165	1	“	"	PUNCT
brj-23849	165	2	dcgan	dcgan	VERB
brj-23849	165	3	enhancement	enhancement	NOUN
brj-23849	165	4	method	method	NOUN
brj-23849	165	5	,	,	PUNCT
brj-23849	165	6	”	"	PUNCT
brj-23849	165	7	bioresources	bioresource	NOUN
brj-23849	165	8	19(4	19(4	NUM
brj-23849	165	9	)	)	PUNCT
brj-23849	165	10	,	,	PUNCT
brj-23849	165	11	9271	9271	NUM
brj-23849	165	12	-	-	SYM
brj-23849	165	13	9284	9284	NUM
brj-23849	165	14	.	.	PUNCT
brj-23849	166	1	9281	9281	NUM
brj-23849	166	2	(	(	PUNCT
brj-23849	166	3	a	a	X
brj-23849	166	4	)	)	PUNCT
brj-23849	166	5	original	original	ADJ
brj-23849	166	6	images	image	NOUN
brj-23849	166	7	(	(	PUNCT
brj-23849	166	8	b	b	NOUN
brj-23849	166	9	)	)	PUNCT
brj-23849	166	10	generate	generate	VERB
brj-23849	166	11	images	image	NOUN
brj-23849	166	12	fig	fig	NOUN
brj-23849	166	13	.	.	PUNCT
brj-23849	167	1	16	16	NUM
brj-23849	167	2	.	.	PUNCT
brj-23849	168	1	comparison	comparison	NOUN
brj-23849	168	2	of	of	ADP
brj-23849	168	3	the	the	DET
brj-23849	168	4	generated	generate	VERB
brj-23849	168	5	images	image	NOUN
brj-23849	168	6	with	with	ADP
brj-23849	168	7	the	the	DET
brj-23849	168	8	original	original	ADJ
brj-23849	168	9	images	image	NOUN
brj-23849	168	10	the	the	DET
brj-23849	168	11	expanded	expand	VERB
brj-23849	168	12	images	image	NOUN
brj-23849	168	13	were	be	AUX
brj-23849	168	14	picked	pick	VERB
brj-23849	168	15	separately	separately	ADV
brj-23849	168	16	to	to	PART
brj-23849	168	17	remove	remove	VERB
brj-23849	168	18	the	the	DET
brj-23849	168	19	images	image	NOUN
brj-23849	168	20	with	with	ADP
brj-23849	168	21	too	too	ADV
brj-23849	168	22	high	high	ADJ
brj-23849	168	23	repetition	repetition	NOUN
brj-23849	168	24	to	to	PART
brj-23849	168	25	obtain	obtain	VERB
brj-23849	168	26	twelve	twelve	NUM
brj-23849	168	27	categories	category	NOUN
brj-23849	168	28	of	of	ADP
brj-23849	168	29	expanded	expand	VERB
brj-23849	168	30	datasets	dataset	NOUN
brj-23849	168	31	,	,	PUNCT
brj-23849	168	32	at	at	ADP
brj-23849	168	33	which	which	DET
brj-23849	168	34	time	time	NOUN
brj-23849	168	35	the	the	DET
brj-23849	168	36	expanded	expand	VERB
brj-23849	168	37	datasets	dataset	NOUN
brj-23849	168	38	reached	reach	VERB
brj-23849	168	39	the	the	DET
brj-23849	168	40	20,000	20,000	NUM
brj-23849	168	41	level	level	NOUN
brj-23849	168	42	,	,	PUNCT
brj-23849	168	43	and	and	CCONJ
brj-23849	168	44	there	there	PRON
brj-23849	168	45	were	be	VERB
brj-23849	168	46	no	no	DET
brj-23849	168	47	real	real	ADJ
brj-23849	168	48	images	image	NOUN
brj-23849	168	49	from	from	ADP
brj-23849	168	50	the	the	DET
brj-23849	168	51	original	original	ADJ
brj-23849	168	52	collection	collection	NOUN
brj-23849	168	53	in	in	ADP
brj-23849	168	54	the	the	DET
brj-23849	168	55	expanded	expand	VERB
brj-23849	168	56	datasets	dataset	NOUN
brj-23849	168	57	.	.	PUNCT
brj-23849	169	1	using	use	VERB
brj-23849	169	2	this	this	DET
brj-23849	169	3	dataset	dataset	NOUN
brj-23849	169	4	to	to	PART
brj-23849	169	5	train	train	VERB
brj-23849	169	6	the	the	DET
brj-23849	169	7	net	net	ADJ
brj-23849	169	8	curtain	curtain	NOUN
brj-23849	169	9	recognition	recognition	NOUN
brj-23849	169	10	algorithm	algorithm	NOUN
brj-23849	169	11	based	base	VERB
brj-23849	169	12	on	on	ADP
brj-23849	169	13	the	the	DET
brj-23849	169	14	convolutional	convolutional	ADJ
brj-23849	169	15	neural	neural	ADJ
brj-23849	169	16	network	network	NOUN
brj-23849	169	17	proposed	propose	VERB
brj-23849	169	18	in	in	ADP
brj-23849	169	19	the	the	DET
brj-23849	169	20	literature	literature	NOUN
brj-23849	169	21	(	(	PUNCT
brj-23849	169	22	gao	gao	PROPN
brj-23849	169	23	et	et	PROPN
brj-23849	169	24	al	al	PROPN
brj-23849	169	25	.	.	PROPN
brj-23849	169	26	2020	2020	NUM
brj-23849	169	27	)	)	PUNCT
brj-23849	169	28	,	,	PUNCT
brj-23849	169	29	the	the	DET
brj-23849	169	30	recognition	recognition	NOUN
brj-23849	169	31	results	result	NOUN
brj-23849	169	32	of	of	ADP
brj-23849	169	33	the	the	DET
brj-23849	169	34	trained	train	VERB
brj-23849	169	35	algorithm	algorithm	NOUN
brj-23849	169	36	for	for	ADP
brj-23849	169	37	real	real	ADJ
brj-23849	169	38	net	net	ADJ
brj-23849	169	39	curtain	curtain	NOUN
brj-23849	169	40	images	image	NOUN
brj-23849	169	41	are	be	AUX
brj-23849	169	42	shown	show	VERB
brj-23849	169	43	in	in	ADP
brj-23849	169	44	fig	fig	NOUN
brj-23849	169	45	.	.	PUNCT
brj-23849	170	1	17	17	NUM
brj-23849	170	2	.	.	PUNCT
brj-23849	171	1	(	(	PUNCT
brj-23849	171	2	a	a	X
brj-23849	171	3	)	)	PUNCT
brj-23849	171	4	(	(	PUNCT
brj-23849	171	5	b	b	X
brj-23849	171	6	)	)	PUNCT
brj-23849	171	7	(	(	PUNCT
brj-23849	171	8	c	c	X
brj-23849	171	9	)	)	PUNCT
brj-23849	171	10	(	(	PUNCT
brj-23849	171	11	d	d	X
brj-23849	171	12	)	)	PUNCT
brj-23849	171	13	(	(	PUNCT
brj-23849	171	14	e	e	NOUN
brj-23849	171	15	)	)	PUNCT
brj-23849	171	16	(	(	PUNCT
brj-23849	171	17	f	f	X
brj-23849	171	18	)	)	PUNCT
brj-23849	171	19	(	(	PUNCT
brj-23849	171	20	g	g	NOUN
brj-23849	171	21	)	)	PUNCT
brj-23849	171	22	(	(	PUNCT
brj-23849	171	23	h	h	NOUN
brj-23849	171	24	)	)	PUNCT
brj-23849	171	25	(	(	PUNCT
brj-23849	171	26	i	i	NOUN
brj-23849	171	27	)	)	PUNCT
brj-23849	171	28	(	(	PUNCT
brj-23849	171	29	j	j	NOUN
brj-23849	171	30	)	)	PUNCT
brj-23849	171	31	(	(	PUNCT
brj-23849	171	32	k	k	NOUN
brj-23849	171	33	)	)	PUNCT
brj-23849	171	34	(	(	PUNCT
brj-23849	171	35	l	l	NOUN
brj-23849	171	36	)	)	PUNCT
brj-23849	171	37	(	(	PUNCT
brj-23849	171	38	m	m	NOUN
brj-23849	171	39	)	)	PUNCT
brj-23849	171	40	(	(	PUNCT
brj-23849	171	41	n	n	CCONJ
brj-23849	171	42	)	)	PUNCT
brj-23849	171	43	(	(	PUNCT
brj-23849	171	44	o	o	NOUN
brj-23849	171	45	)	)	PUNCT
brj-23849	171	46	(	(	PUNCT
brj-23849	171	47	p	p	X
brj-23849	171	48	)	)	PUNCT
brj-23849	171	49	(	(	PUNCT
brj-23849	171	50	q	q	X
brj-23849	171	51	)	)	PUNCT
brj-23849	171	52	(	(	PUNCT
brj-23849	171	53	r	r	NOUN
brj-23849	171	54	)	)	PUNCT
brj-23849	171	55	(	(	PUNCT
brj-23849	171	56	s	s	NOUN
brj-23849	171	57	)	)	PUNCT
brj-23849	171	58	(	(	PUNCT
brj-23849	171	59	t	t	NOUN
brj-23849	171	60	)	)	PUNCT
brj-23849	171	61	peerreviewed	peerreviewe	VERB
brj-23849	171	62	article	article	NOUN
brj-23849	171	63	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	171	64	teng	teng	PROPN
brj-23849	171	65	et	et	PROPN
brj-23849	171	66	al	al	PROPN
brj-23849	171	67	.	.	PROPN
brj-23849	172	1	(	(	PUNCT
brj-23849	172	2	2024	2024	NUM
brj-23849	172	3	)	)	PUNCT
brj-23849	172	4	.	.	PUNCT
brj-23849	173	1	“	"	PUNCT
brj-23849	173	2	dcgan	dcgan	VERB
brj-23849	173	3	enhancement	enhancement	NOUN
brj-23849	173	4	method	method	NOUN
brj-23849	173	5	,	,	PUNCT
brj-23849	173	6	”	"	PUNCT
brj-23849	173	7	bioresources	bioresource	NOUN
brj-23849	173	8	19(4	19(4	NUM
brj-23849	173	9	)	)	PUNCT
brj-23849	173	10	,	,	PUNCT
brj-23849	173	11	9271	9271	NUM
brj-23849	173	12	-	-	SYM
brj-23849	173	13	9284	9284	NUM
brj-23849	173	14	.	.	PUNCT
brj-23849	174	1	9282	9282	NUM
brj-23849	174	2	(	(	PUNCT
brj-23849	174	3	u	u	NOUN
brj-23849	174	4	)	)	PUNCT
brj-23849	174	5	(	(	PUNCT
brj-23849	174	6	v	v	NOUN
brj-23849	174	7	)	)	PUNCT
brj-23849	174	8	(	(	PUNCT
brj-23849	174	9	w	w	NOUN
brj-23849	174	10	)	)	PUNCT
brj-23849	174	11	(	(	PUNCT
brj-23849	174	12	x	x	X
brj-23849	174	13	)	)	PUNCT
brj-23849	174	14	fig	fig	NOUN
brj-23849	174	15	.	.	PUNCT
brj-23849	175	1	17	17	NUM
brj-23849	175	2	.	.	PUNCT
brj-23849	176	1	recognition	recognition	NOUN
brj-23849	176	2	effect	effect	NOUN
brj-23849	176	3	diagram	diagram	VERB
brj-23849	176	4	the	the	DET
brj-23849	176	5	recognition	recognition	NOUN
brj-23849	176	6	results	result	NOUN
brj-23849	176	7	showed	show	VERB
brj-23849	176	8	that	that	SCONJ
brj-23849	176	9	the	the	DET
brj-23849	176	10	image	image	NOUN
brj-23849	176	11	recognition	recognition	NOUN
brj-23849	176	12	algorithm	algorithm	NOUN
brj-23849	176	13	trained	train	VERB
brj-23849	176	14	using	use	VERB
brj-23849	176	15	the	the	DET
brj-23849	176	16	expanded	expand	VERB
brj-23849	176	17	net	net	ADJ
brj-23849	176	18	curtain	curtain	NOUN
brj-23849	176	19	dataset	dataset	NOUN
brj-23849	176	20	of	of	ADP
brj-23849	176	21	this	this	DET
brj-23849	176	22	method	method	NOUN
brj-23849	176	23	can	can	AUX
brj-23849	176	24	achieve	achieve	VERB
brj-23849	176	25	good	good	ADJ
brj-23849	176	26	recognition	recognition	NOUN
brj-23849	176	27	under	under	ADP
brj-23849	176	28	different	different	ADJ
brj-23849	176	29	scenes	scene	NOUN
brj-23849	176	30	and	and	CCONJ
brj-23849	176	31	lighting	lighting	NOUN
brj-23849	176	32	conditions	condition	NOUN
brj-23849	176	33	.	.	PUNCT
brj-23849	177	1	the	the	DET
brj-23849	177	2	expanded	expand	VERB
brj-23849	177	3	mesh	mesh	NOUN
brj-23849	177	4	dataset	dataset	NOUN
brj-23849	177	5	includes	include	VERB
brj-23849	177	6	the	the	DET
brj-23849	177	7	expanded	expand	VERB
brj-23849	177	8	images	image	NOUN
brj-23849	177	9	obtained	obtain	VERB
brj-23849	177	10	by	by	ADP
brj-23849	177	11	training	train	VERB
brj-23849	177	12	the	the	DET
brj-23849	177	13	gan	gan	NOUN
brj-23849	177	14	with	with	ADP
brj-23849	177	15	four	four	NUM
brj-23849	177	16	cropped	cropped	ADJ
brj-23849	177	17	images	image	NOUN
brj-23849	177	18	from	from	ADP
brj-23849	177	19	fig	fig	NOUN
brj-23849	177	20	.	.	PUNCT
brj-23849	178	1	17(a	17(a	NUM
brj-23849	178	2	)	)	PUNCT
brj-23849	178	3	through	through	ADP
brj-23849	178	4	(	(	PUNCT
brj-23849	178	5	k	k	NOUN
brj-23849	178	6	)	)	PUNCT
brj-23849	178	7	,	,	PUNCT
brj-23849	178	8	excluding	exclude	VERB
brj-23849	178	9	the	the	DET
brj-23849	178	10	image	image	NOUN
brj-23849	178	11	parts	part	NOUN
brj-23849	178	12	in	in	ADP
brj-23849	178	13	(	(	PUNCT
brj-23849	178	14	l	l	NOUN
brj-23849	178	15	)	)	PUNCT
brj-23849	178	16	through	through	ADP
brj-23849	178	17	(	(	PUNCT
brj-23849	178	18	x	x	NOUN
brj-23849	178	19	)	)	PUNCT
brj-23849	178	20	.	.	PUNCT
brj-23849	179	1	the	the	DET
brj-23849	179	2	recognition	recognition	NOUN
brj-23849	179	3	images	image	NOUN
brj-23849	179	4	(	(	PUNCT
brj-23849	179	5	a	a	X
brj-23849	179	6	)	)	PUNCT
brj-23849	179	7	through	through	ADP
brj-23849	179	8	(	(	PUNCT
brj-23849	179	9	k	k	X
brj-23849	179	10	)	)	PUNCT
brj-23849	179	11	achieved	achieve	VERB
brj-23849	179	12	a	a	DET
brj-23849	179	13	high	high	ADJ
brj-23849	179	14	accuracy	accuracy	NOUN
brj-23849	179	15	rate	rate	NOUN
brj-23849	179	16	,	,	PUNCT
brj-23849	179	17	which	which	PRON
brj-23849	179	18	shows	show	VERB
brj-23849	179	19	that	that	SCONJ
brj-23849	179	20	the	the	DET
brj-23849	179	21	images	image	NOUN
brj-23849	179	22	generated	generate	VERB
brj-23849	179	23	by	by	ADP
brj-23849	179	24	the	the	DET
brj-23849	179	25	algorithm	algorithm	NOUN
brj-23849	179	26	met	meet	VERB
brj-23849	179	27	the	the	DET
brj-23849	179	28	requirements	requirement	NOUN
brj-23849	179	29	and	and	CCONJ
brj-23849	179	30	reached	reach	VERB
brj-23849	179	31	the	the	DET
brj-23849	179	32	quality	quality	NOUN
brj-23849	179	33	standards	standard	NOUN
brj-23849	179	34	.	.	PUNCT
brj-23849	180	1	figures	figure	NOUN
brj-23849	180	2	17(l	17(l	NUM
brj-23849	180	3	)	)	PUNCT
brj-23849	180	4	through	through	ADP
brj-23849	180	5	(	(	PUNCT
brj-23849	180	6	x	x	X
brj-23849	180	7	)	)	PUNCT
brj-23849	180	8	also	also	ADV
brj-23849	180	9	obtained	obtain	VERB
brj-23849	180	10	high	high	ADJ
brj-23849	180	11	recognition	recognition	NOUN
brj-23849	180	12	rates	rate	NOUN
brj-23849	180	13	,	,	PUNCT
brj-23849	180	14	indicating	indicate	VERB
brj-23849	180	15	that	that	SCONJ
brj-23849	180	16	the	the	DET
brj-23849	180	17	expansion	expansion	NOUN
brj-23849	180	18	of	of	ADP
brj-23849	180	19	the	the	DET
brj-23849	180	20	dataset	dataset	NOUN
brj-23849	180	21	improved	improve	VERB
brj-23849	180	22	the	the	DET
brj-23849	180	23	generalization	generalization	NOUN
brj-23849	180	24	ability	ability	NOUN
brj-23849	180	25	to	to	PART
brj-23849	180	26	recognize	recognize	VERB
brj-23849	180	27	the	the	DET
brj-23849	180	28	localized	localized	ADJ
brj-23849	180	29	mesh	mesh	NOUN
brj-23849	180	30	curtain	curtain	NOUN
brj-23849	180	31	.	.	PUNCT
brj-23849	181	1	figures	figure	NOUN
brj-23849	181	2	17(d	17(d	NUM
brj-23849	181	3	)	)	PUNCT
brj-23849	181	4	and	and	CCONJ
brj-23849	181	5	(	(	PUNCT
brj-23849	181	6	e	e	NOUN
brj-23849	181	7	)	)	PUNCT
brj-23849	181	8	are	be	AUX
brj-23849	181	9	images	image	NOUN
brj-23849	181	10	taken	take	VERB
brj-23849	181	11	at	at	ADP
brj-23849	181	12	different	different	ADJ
brj-23849	181	13	angles	angle	NOUN
brj-23849	181	14	,	,	PUNCT
brj-23849	181	15	and	and	CCONJ
brj-23849	181	16	(	(	PUNCT
brj-23849	181	17	j	j	NOUN
brj-23849	181	18	)	)	PUNCT
brj-23849	181	19	was	be	AUX
brj-23849	181	20	obtained	obtain	VERB
brj-23849	181	21	from	from	ADP
brj-23849	181	22	(	(	PUNCT
brj-23849	181	23	k	k	NOUN
brj-23849	181	24	)	)	PUNCT
brj-23849	181	25	after	after	ADP
brj-23849	181	26	flipping	flip	VERB
brj-23849	181	27	.	.	PUNCT
brj-23849	182	1	all	all	DET
brj-23849	182	2	four	four	NUM
brj-23849	182	3	images	image	NOUN
brj-23849	182	4	achieved	achieve	VERB
brj-23849	182	5	good	good	ADJ
brj-23849	182	6	recognition	recognition	NOUN
brj-23849	182	7	results	result	NOUN
brj-23849	182	8	,	,	PUNCT
brj-23849	182	9	which	which	PRON
brj-23849	182	10	indicates	indicate	VERB
brj-23849	182	11	that	that	SCONJ
brj-23849	182	12	the	the	DET
brj-23849	182	13	algorithm	algorithm	NOUN
brj-23849	182	14	has	have	VERB
brj-23849	182	15	a	a	DET
brj-23849	182	16	strong	strong	ADJ
brj-23849	182	17	generalization	generalization	NOUN
brj-23849	182	18	ability	ability	NOUN
brj-23849	182	19	for	for	ADP
brj-23849	182	20	the	the	DET
brj-23849	182	21	recognition	recognition	NOUN
brj-23849	182	22	of	of	ADP
brj-23849	182	23	images	image	NOUN
brj-23849	182	24	at	at	ADP
brj-23849	182	25	different	different	ADJ
brj-23849	182	26	angles	angle	NOUN
brj-23849	182	27	after	after	ADP
brj-23849	182	28	training	training	NOUN
brj-23849	182	29	with	with	ADP
brj-23849	182	30	the	the	DET
brj-23849	182	31	expanded	expand	VERB
brj-23849	182	32	dataset	dataset	NOUN
brj-23849	182	33	.	.	PUNCT
brj-23849	183	1	future	future	ADJ
brj-23849	183	2	directions	direction	NOUN
brj-23849	183	3	in	in	ADP
brj-23849	183	4	pest	pest	NOUN
brj-23849	183	5	control	control	VERB
brj-23849	183	6	the	the	DET
brj-23849	183	7	current	current	ADJ
brj-23849	183	8	trajectory	trajectory	NOUN
brj-23849	183	9	of	of	ADP
brj-23849	183	10	pest	pest	NOUN
brj-23849	183	11	control	control	NOUN
brj-23849	183	12	in	in	ADP
brj-23849	183	13	agriculture	agriculture	NOUN
brj-23849	183	14	is	be	AUX
brj-23849	183	15	heavily	heavily	ADV
brj-23849	183	16	influenced	influence	VERB
brj-23849	183	17	by	by	ADP
brj-23849	183	18	advancements	advancement	NOUN
brj-23849	183	19	in	in	ADP
brj-23849	183	20	deep	deep	ADJ
brj-23849	183	21	learning	learning	NOUN
brj-23849	183	22	and	and	CCONJ
brj-23849	183	23	image	image	NOUN
brj-23849	183	24	processing	processing	NOUN
brj-23849	183	25	.	.	PUNCT
brj-23849	184	1	key	key	ADJ
brj-23849	184	2	future	future	ADJ
brj-23849	184	3	developments	development	NOUN
brj-23849	184	4	include	include	VERB
brj-23849	184	5	refining	refine	VERB
brj-23849	184	6	image	image	NOUN
brj-23849	184	7	generation	generation	NOUN
brj-23849	184	8	algorithms	algorithm	NOUN
brj-23849	184	9	,	,	PUNCT
brj-23849	184	10	integrating	integrate	VERB
brj-23849	184	11	multimodal	multimodal	NOUN
brj-23849	184	12	data	datum	NOUN
brj-23849	184	13	sources	source	NOUN
brj-23849	184	14	,	,	PUNCT
brj-23849	184	15	enabling	enable	VERB
brj-23849	184	16	realtime	realtime	NOUN
brj-23849	184	17	deployment	deployment	NOUN
brj-23849	184	18	,	,	PUNCT
brj-23849	184	19	addressing	address	VERB
brj-23849	184	20	ethical	ethical	ADJ
brj-23849	184	21	and	and	CCONJ
brj-23849	184	22	environmental	environmental	ADJ
brj-23849	184	23	concerns	concern	NOUN
brj-23849	184	24	,	,	PUNCT
brj-23849	184	25	and	and	CCONJ
brj-23849	184	26	fostering	foster	VERB
brj-23849	184	27	collaborative	collaborative	ADJ
brj-23849	184	28	research	research	NOUN
brj-23849	184	29	efforts	effort	NOUN
brj-23849	184	30	.	.	PUNCT
brj-23849	185	1	embracing	embrace	VERB
brj-23849	185	2	these	these	DET
brj-23849	185	3	avenues	avenue	NOUN
brj-23849	185	4	will	will	AUX
brj-23849	185	5	lead	lead	VERB
brj-23849	185	6	to	to	ADP
brj-23849	185	7	more	more	ADV
brj-23849	185	8	effective	effective	ADJ
brj-23849	185	9	and	and	CCONJ
brj-23849	185	10	sustainable	sustainable	ADJ
brj-23849	185	11	pest	pest	NOUN
brj-23849	185	12	management	management	NOUN
brj-23849	185	13	practices	practice	NOUN
brj-23849	185	14	in	in	ADP
brj-23849	185	15	agriculture	agriculture	NOUN
brj-23849	185	16	.	.	PUNCT
brj-23849	186	1	conclusions	conclusion	NOUN
brj-23849	186	2	1	1	NUM
brj-23849	186	3	.	.	PUNCT
brj-23849	186	4	improved	improve	VERB
brj-23849	186	5	dcgan	dcgan	NOUN
brj-23849	186	6	for	for	ADP
brj-23849	186	7	dataset	dataset	ADJ
brj-23849	186	8	expansion	expansion	NOUN
brj-23849	186	9	:	:	PUNCT
brj-23849	186	10	an	an	DET
brj-23849	186	11	image	image	NOUN
brj-23849	186	12	data	data	NOUN
brj-23849	186	13	enhancement	enhancement	NOUN
brj-23849	186	14	algorithm	algorithm	NOUN
brj-23849	186	15	based	base	VERB
brj-23849	186	16	on	on	ADP
brj-23849	186	17	improved	improved	ADJ
brj-23849	186	18	dcgan	dcgan	NOUN
brj-23849	186	19	(	(	PUNCT
brj-23849	186	20	deep	deep	ADJ
brj-23849	186	21	convolutional	convolutional	ADJ
brj-23849	186	22	generative	generative	ADJ
brj-23849	186	23	adversarial	adversarial	ADJ
brj-23849	186	24	network	network	NOUN
brj-23849	186	25	)	)	PUNCT
brj-23849	186	26	was	be	AUX
brj-23849	186	27	proposed	propose	VERB
brj-23849	186	28	to	to	PART
brj-23849	186	29	expand	expand	VERB
brj-23849	186	30	the	the	DET
brj-23849	186	31	american	american	PROPN
brj-23849	186	32	hyphantria	hyphantria	PROPN
brj-23849	186	33	cunea	cunea	PROPN
brj-23849	186	34	larvae	larvae	PROPN
brj-23849	186	35	net	net	ADJ
brj-23849	186	36	curtain	curtain	NOUN
brj-23849	186	37	dataset	dataset	NOUN
brj-23849	186	38	.	.	PUNCT
brj-23849	187	1	the	the	DET
brj-23849	187	2	collected	collect	VERB
brj-23849	187	3	original	original	ADJ
brj-23849	187	4	images	image	NOUN
brj-23849	187	5	were	be	AUX
brj-23849	187	6	cropped	crop	VERB
brj-23849	187	7	to	to	ADP
brj-23849	187	8	a	a	DET
brj-23849	187	9	resolution	resolution	NOUN
brj-23849	187	10	of	of	ADP
brj-23849	187	11	64	64	NUM
brj-23849	187	12	×	×	NOUN
brj-23849	187	13	64	64	NUM
brj-23849	187	14	to	to	PART
brj-23849	187	15	handle	handle	VERB
brj-23849	187	16	the	the	DET
brj-23849	187	17	large	large	ADJ
brj-23849	187	18	resolution	resolution	NOUN
brj-23849	187	19	and	and	CCONJ
brj-23849	187	20	complex	complex	ADJ
brj-23849	187	21	composition	composition	NOUN
brj-23849	187	22	of	of	ADP
brj-23849	187	23	the	the	DET
brj-23849	187	24	larvae	larvae	NOUN
brj-23849	187	25	net	net	ADJ
brj-23849	187	26	curtain	curtain	NOUN
brj-23849	187	27	images	image	NOUN
brj-23849	187	28	.	.	PUNCT
brj-23849	188	1	2	2	X
brj-23849	188	2	.	.	X
brj-23849	188	3	training	training	NOUN
brj-23849	188	4	for	for	ADP
brj-23849	188	5	color	color	NOUN
brj-23849	188	6	differences	difference	NOUN
brj-23849	188	7	:	:	PUNCT
brj-23849	188	8	images	image	NOUN
brj-23849	188	9	with	with	ADP
brj-23849	188	10	significant	significant	ADJ
brj-23849	188	11	color	color	NOUN
brj-23849	188	12	differences	difference	NOUN
brj-23849	188	13	under	under	ADP
brj-23849	188	14	various	various	ADJ
brj-23849	188	15	conditions	condition	NOUN
brj-23849	188	16	were	be	AUX
brj-23849	188	17	trained	train	VERB
brj-23849	188	18	separately	separately	ADV
brj-23849	188	19	,	,	PUNCT
brj-23849	188	20	ensuring	ensure	VERB
brj-23849	188	21	the	the	DET
brj-23849	188	22	expanded	expand	VERB
brj-23849	188	23	dataset	dataset	NOUN
brj-23849	188	24	maintained	maintain	VERB
brj-23849	188	25	high	high	ADJ
brj-23849	188	26	image	image	NOUN
brj-23849	188	27	quality	quality	NOUN
brj-23849	188	28	.	.	PUNCT
brj-23849	189	1	3	3	X
brj-23849	189	2	.	.	X
brj-23849	189	3	algorithm	algorithm	NOUN
brj-23849	189	4	optimization	optimization	NOUN
brj-23849	189	5	:	:	PUNCT
brj-23849	189	6	the	the	DET
brj-23849	189	7	deconvolution	deconvolution	NOUN
brj-23849	189	8	layer	layer	NOUN
brj-23849	189	9	was	be	AUX
brj-23849	189	10	eliminated	eliminate	VERB
brj-23849	189	11	,	,	PUNCT
brj-23849	189	12	and	and	CCONJ
brj-23849	189	13	a	a	DET
brj-23849	189	14	resize	resize	NOUN
brj-23849	189	15	convolution	convolution	NOUN
brj-23849	189	16	layer	layer	NOUN
brj-23849	189	17	was	be	AUX
brj-23849	189	18	introduced	introduce	VERB
brj-23849	189	19	to	to	PART
brj-23849	189	20	reduce	reduce	VERB
brj-23849	189	21	the	the	DET
brj-23849	189	22	checkerboard	checkerboard	NOUN
brj-23849	189	23	effect	effect	NOUN
brj-23849	189	24	and	and	CCONJ
brj-23849	189	25	accelerate	accelerate	VERB
brj-23849	189	26	training	training	NOUN
brj-23849	189	27	.	.	PUNCT
brj-23849	190	1	a	a	DET
brj-23849	190	2	dropout	dropout	NOUN
brj-23849	190	3	layer	layer	NOUN
brj-23849	190	4	was	be	AUX
brj-23849	190	5	added	add	VERB
brj-23849	190	6	to	to	PART
brj-23849	190	7	improve	improve	VERB
brj-23849	190	8	the	the	DET
brj-23849	190	9	stability	stability	NOUN
brj-23849	190	10	of	of	ADP
brj-23849	190	11	training	training	NOUN
brj-23849	190	12	.	.	PUNCT
brj-23849	191	1	using	use	VERB
brj-23849	191	2	the	the	DET
brj-23849	191	3	leakyrelu	leakyrelu	ADJ
brj-23849	191	4	function	function	NOUN
brj-23849	191	5	instead	instead	ADV
brj-23849	191	6	of	of	ADP
brj-23849	191	7	the	the	DET
brj-23849	191	8	relu	relu	NOUN
brj-23849	191	9	function	function	NOUN
brj-23849	191	10	avoided	avoid	VERB
brj-23849	191	11	neuron	neuron	NOUN
brj-23849	191	12	necrosis	necrosis	NOUN
brj-23849	191	13	.	.	PUNCT
brj-23849	192	1	peerreviewed	peerreviewe	VERB
brj-23849	192	2	article	article	NOUN
brj-23849	192	3	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	192	4	teng	teng	PROPN
brj-23849	192	5	et	et	PROPN
brj-23849	192	6	al	al	PROPN
brj-23849	192	7	.	.	PROPN
brj-23849	193	1	(	(	PUNCT
brj-23849	193	2	2024	2024	NUM
brj-23849	193	3	)	)	PUNCT
brj-23849	193	4	.	.	PUNCT
brj-23849	194	1	“	"	PUNCT
brj-23849	194	2	dcgan	dcgan	VERB
brj-23849	194	3	enhancement	enhancement	NOUN
brj-23849	194	4	method	method	NOUN
brj-23849	194	5	,	,	PUNCT
brj-23849	194	6	”	"	PUNCT
brj-23849	194	7	bioresources	bioresource	NOUN
brj-23849	194	8	19(4	19(4	NUM
brj-23849	194	9	)	)	PUNCT
brj-23849	194	10	,	,	PUNCT
brj-23849	194	11	9271	9271	NUM
brj-23849	194	12	-	-	SYM
brj-23849	194	13	9284	9284	NUM
brj-23849	194	14	.	.	PUNCT
brj-23849	195	1	9283	9283	NUM
brj-23849	195	2	4	4	NUM
brj-23849	195	3	.	.	PUNCT
brj-23849	195	4	enhanced	enhance	VERB
brj-23849	195	5	neural	neural	ADJ
brj-23849	195	6	network	network	NOUN
brj-23849	195	7	generalization	generalization	NOUN
brj-23849	195	8	:	:	PUNCT
brj-23849	195	9	the	the	DET
brj-23849	195	10	improved	improved	ADJ
brj-23849	195	11	dcgan	dcgan	NOUN
brj-23849	195	12	network	network	NOUN
brj-23849	195	13	was	be	AUX
brj-23849	195	14	trained	train	VERB
brj-23849	195	15	to	to	PART
brj-23849	195	16	generate	generate	VERB
brj-23849	195	17	the	the	DET
brj-23849	195	18	final	final	ADJ
brj-23849	195	19	expanded	expand	VERB
brj-23849	195	20	dataset	dataset	NOUN
brj-23849	195	21	.	.	PUNCT
brj-23849	196	1	using	use	VERB
brj-23849	196	2	this	this	DET
brj-23849	196	3	dataset	dataset	NOUN
brj-23849	196	4	to	to	PART
brj-23849	196	5	train	train	VERB
brj-23849	196	6	existing	exist	VERB
brj-23849	196	7	recognition	recognition	NOUN
brj-23849	196	8	algorithms	algorithm	NOUN
brj-23849	196	9	significantly	significantly	ADV
brj-23849	196	10	improved	improve	VERB
brj-23849	196	11	the	the	DET
brj-23849	196	12	generalization	generalization	NOUN
brj-23849	196	13	ability	ability	NOUN
brj-23849	196	14	of	of	ADP
brj-23849	196	15	the	the	DET
brj-23849	196	16	neural	neural	ADJ
brj-23849	196	17	network	network	NOUN
brj-23849	196	18	,	,	PUNCT
brj-23849	196	19	achieving	achieve	VERB
brj-23849	196	20	high	high	ADJ
brj-23849	196	21	recognition	recognition	NOUN
brj-23849	196	22	accuracy	accuracy	NOUN
brj-23849	196	23	.	.	PUNCT
brj-23849	197	1	acknowledgments	acknowledgment	NOUN
brj-23849	197	2	thank	thank	VERB
brj-23849	197	3	you	you	PRON
brj-23849	197	4	to	to	ADP
brj-23849	197	5	the	the	DET
brj-23849	197	6	reviewers	reviewer	NOUN
brj-23849	197	7	for	for	ADP
brj-23849	197	8	their	their	PRON
brj-23849	197	9	patience	patience	NOUN
brj-23849	197	10	and	and	CCONJ
brj-23849	197	11	professionalism	professionalism	NOUN
brj-23849	197	12	,	,	PUNCT
brj-23849	197	13	and	and	CCONJ
brj-23849	197	14	for	for	ADP
brj-23849	197	15	assistance	assistance	NOUN
brj-23849	197	16	with	with	ADP
brj-23849	197	17	this	this	DET
brj-23849	197	18	work	work	NOUN
brj-23849	197	19	.	.	PUNCT
brj-23849	198	1	funding	fund	VERB
brj-23849	198	2	this	this	DET
brj-23849	198	3	paper	paper	NOUN
brj-23849	198	4	was	be	AUX
brj-23849	198	5	supported	support	VERB
brj-23849	198	6	by	by	ADP
brj-23849	198	7	the	the	DET
brj-23849	198	8	national	national	ADJ
brj-23849	198	9	key	key	ADJ
brj-23849	198	10	research	research	NOUN
brj-23849	198	11	and	and	CCONJ
brj-23849	198	12	development	development	NOUN
brj-23849	198	13	program	program	NOUN
brj-23849	198	14	,	,	PUNCT
brj-23849	198	15	and	and	CCONJ
brj-23849	198	16	the	the	DET
brj-23849	198	17	subject	subject	ADJ
brj-23849	198	18	number	number	NOUN
brj-23849	198	19	is	be	AUX
brj-23849	198	20	2022yfd2001905	2022yfd2001905	PROPN
brj-23849	198	21	.	.	PUNCT
brj-23849	199	1	references	reference	NOUN
brj-23849	199	2	cited	cite	VERB
brj-23849	199	3	de	de	PROPN
brj-23849	199	4	andrade	andrade	PROPN
brj-23849	199	5	,	,	PUNCT
brj-23849	199	6	a.	a.	NOUN
brj-23849	199	7	(	(	PUNCT
brj-23849	199	8	2019	2019	NUM
brj-23849	199	9	)	)	PUNCT
brj-23849	199	10	.	.	PUNCT
brj-23849	200	1	“	"	PUNCT
brj-23849	200	2	best	good	ADJ
brj-23849	200	3	practices	practice	NOUN
brj-23849	200	4	for	for	ADP
brj-23849	200	5	convolutional	convolutional	ADJ
brj-23849	200	6	neural	neural	ADJ
brj-23849	200	7	networks	network	NOUN
brj-23849	200	8	applied	apply	VERB
brj-23849	200	9	to	to	PART
brj-23849	200	10	object	object	VERB
brj-23849	200	11	recognition	recognition	NOUN
brj-23849	200	12	in	in	ADP
brj-23849	200	13	images	image	NOUN
brj-23849	200	14	,	,	PUNCT
brj-23849	200	15	”	"	PUNCT
brj-23849	200	16	arxiv	arxiv	PROPN
brj-23849	200	17	1910.13029	1910.13029	PROPN
brj-23849	200	18	.	.	PUNCT
brj-23849	201	1	doi	doi	NOUN
brj-23849	201	2	:	:	PUNCT
brj-23849	201	3	10.48550	10.48550	NUM
brj-23849	201	4	/	/	SYM
brj-23849	201	5	arxiv.1910.13029	arxiv.1910.13029	PROPN
brj-23849	201	6	ding	ding	NOUN
brj-23849	201	7	,	,	PUNCT
brj-23849	201	8	j.	j.	PROPN
brj-23849	201	9	,	,	PUNCT
brj-23849	201	10	li	li	PROPN
brj-23849	201	11	,	,	PUNCT
brj-23849	201	12	x.	x.	PROPN
brj-23849	201	13	,	,	PUNCT
brj-23849	201	14	kang	kang	PROPN
brj-23849	201	15	,	,	PUNCT
brj-23849	201	16	x.	x.	PROPN
brj-23849	201	17	,	,	PUNCT
brj-23849	201	18	and	and	CCONJ
brj-23849	201	19	gudivada	gudivada	PROPN
brj-23849	201	20	,	,	PUNCT
brj-23849	201	21	v.	v.	ADP
brj-23849	201	22	n.	n.	PROPN
brj-23849	201	23	(	(	PUNCT
brj-23849	201	24	2019	2019	NUM
brj-23849	201	25	)	)	PUNCT
brj-23849	201	26	.	.	PUNCT
brj-23849	202	1	“	"	PUNCT
brj-23849	202	2	a	a	DET
brj-23849	202	3	case	case	NOUN
brj-23849	202	4	study	study	NOUN
brj-23849	202	5	of	of	ADP
brj-23849	202	6	the	the	DET
brj-23849	202	7	augmentation	augmentation	NOUN
brj-23849	202	8	and	and	CCONJ
brj-23849	202	9	evaluation	evaluation	NOUN
brj-23849	202	10	of	of	ADP
brj-23849	202	11	training	training	NOUN
brj-23849	202	12	data	datum	NOUN
brj-23849	202	13	for	for	ADP
brj-23849	202	14	deep	deep	ADJ
brj-23849	202	15	learning	learning	NOUN
brj-23849	202	16	,	,	PUNCT
brj-23849	202	17	”	"	PUNCT
brj-23849	202	18	journal	journal	NOUN
brj-23849	202	19	of	of	ADP
brj-23849	202	20	data	datum	NOUN
brj-23849	202	21	and	and	CCONJ
brj-23849	202	22	information	information	NOUN
brj-23849	202	23	quality	quality	NOUN
brj-23849	202	24	11(4	11(4	NUM
brj-23849	202	25	)	)	PUNCT
brj-23849	202	26	,	,	PUNCT
brj-23849	202	27	article	article	NOUN
brj-23849	202	28	20	20	NUM
brj-23849	202	29	.	.	PUNCT
brj-23849	203	1	doi	doi	NOUN
brj-23849	203	2	:	:	PUNCT
brj-23849	203	3	10.1145/3317573	10.1145/3317573	NUM
brj-23849	203	4	gao	gao	PROPN
brj-23849	203	5	,	,	PUNCT
brj-23849	203	6	y.	y.	PROPN
brj-23849	203	7	,	,	PUNCT
brj-23849	203	8	zhao	zhao	PROPN
brj-23849	203	9	,	,	PUNCT
brj-23849	203	10	y.	y.	PROPN
brj-23849	203	11	,	,	PUNCT
brj-23849	203	12	ji	ji	PROPN
brj-23849	203	13	,	,	PUNCT
brj-23849	203	14	y.	y.	PROPN
brj-23849	203	15	,	,	PUNCT
brj-23849	203	16	zhao	zhao	PROPN
brj-23849	203	17	,	,	PUNCT
brj-23849	203	18	d.	d.	PROPN
brj-23849	203	19	,	,	PUNCT
brj-23849	203	20	wang	wang	PROPN
brj-23849	203	21	,	,	PUNCT
brj-23849	203	22	c.	c.	PROPN
brj-23849	203	23	,	,	PUNCT
brj-23849	203	24	and	and	CCONJ
brj-23849	203	25	sun	sun	NOUN
brj-23849	203	26	,	,	PUNCT
brj-23849	203	27	q.	q.	PROPN
brj-23849	203	28	(	(	PUNCT
brj-23849	203	29	2020	2020	NUM
brj-23849	203	30	)	)	PUNCT
brj-23849	203	31	.	.	PUNCT
brj-23849	204	1	“	"	PUNCT
brj-23849	204	2	a	a	DET
brj-23849	204	3	screen	screen	NOUN
brj-23849	204	4	location	location	NOUN
brj-23849	204	5	method	method	NOUN
brj-23849	204	6	for	for	ADP
brj-23849	204	7	treating	treat	VERB
brj-23849	204	8	american	american	PROPN
brj-23849	204	9	hyphantria	hyphantria	PROPN
brj-23849	204	10	cunea	cunea	PROPN
brj-23849	204	11	larvae	larvae	PROPN
brj-23849	204	12	using	use	VERB
brj-23849	204	13	convolutional	convolutional	ADJ
brj-23849	204	14	neural	neural	ADJ
brj-23849	204	15	network	network	NOUN
brj-23849	204	16	,	,	PUNCT
brj-23849	204	17	”	"	PUNCT
brj-23849	204	18	mathematical	mathematical	ADJ
brj-23849	204	19	problems	problem	NOUN
brj-23849	204	20	in	in	ADP
brj-23849	204	21	engineering	engineering	NOUN
brj-23849	204	22	2020	2020	NUM
brj-23849	204	23	,	,	PUNCT
brj-23849	204	24	article	article	NOUN
brj-23849	204	25	i	i	PROPN
brj-23849	204	26	d	d	PROPN
brj-23849	204	27	3874546	3874546	NUM
brj-23849	204	28	.	.	PUNCT
brj-23849	205	1	doi	doi	NOUN
brj-23849	205	2	:	:	PUNCT
brj-23849	205	3	10.1155/2020/3874546	10.1155/2020/3874546	NUM
brj-23849	205	4	goodfellow	goodfellow	NOUN
brj-23849	205	5	,	,	PUNCT
brj-23849	205	6	i.	i.	NOUN
brj-23849	205	7	,	,	PUNCT
brj-23849	205	8	pouget	pouget	NOUN
brj-23849	205	9	-	-	PUNCT
brj-23849	205	10	abadie	abadie	ADJ
brj-23849	205	11	,	,	PUNCT
brj-23849	205	12	j.	j.	PROPN
brj-23849	205	13	,	,	PUNCT
brj-23849	205	14	mirza	mirza	PROPN
brj-23849	205	15	,	,	PUNCT
brj-23849	205	16	m.	m.	NOUN
brj-23849	205	17	,	,	PUNCT
brj-23849	205	18	xu	xu	PROPN
brj-23849	205	19	,	,	PUNCT
brj-23849	205	20	b.	b.	PROPN
brj-23849	205	21	,	,	PUNCT
brj-23849	205	22	warde	warde	PROPN
brj-23849	205	23	-	-	PUNCT
brj-23849	205	24	farley	farley	PROPN
brj-23849	205	25	,	,	PUNCT
brj-23849	205	26	d.	d.	PROPN
brj-23849	205	27	,	,	PUNCT
brj-23849	205	28	ozair	ozair	PROPN
brj-23849	205	29	,	,	PUNCT
brj-23849	205	30	s.	s.	PROPN
brj-23849	205	31	,	,	PUNCT
brj-23849	205	32	courville	courville	NOUN
brj-23849	205	33	,	,	PUNCT
brj-23849	205	34	a.	a.	NOUN
brj-23849	205	35	,	,	PUNCT
brj-23849	205	36	and	and	CCONJ
brj-23849	205	37	bengio	bengio	PROPN
brj-23849	205	38	,	,	PUNCT
brj-23849	205	39	y.	y.	PROPN
brj-23849	205	40	(	(	PUNCT
brj-23849	205	41	2014	2014	NUM
brj-23849	205	42	)	)	PUNCT
brj-23849	205	43	.	.	PUNCT
brj-23849	206	1	“	"	PUNCT
brj-23849	206	2	generative	generative	ADJ
brj-23849	206	3	adversarial	adversarial	ADJ
brj-23849	206	4	nets	net	NOUN
brj-23849	206	5	,	,	PUNCT
brj-23849	206	6	”	"	PUNCT
brj-23849	206	7	in	in	ADP
brj-23849	206	8	:	:	PUNCT
brj-23849	206	9	advances	advance	NOUN
brj-23849	206	10	in	in	ADP
brj-23849	206	11	neural	neural	ADJ
brj-23849	206	12	information	information	NOUN
brj-23849	206	13	processing	processing	NOUN
brj-23849	206	14	systems	system	NOUN
brj-23849	206	15	,	,	PUNCT
brj-23849	206	16	curran	curran	PROPN
brj-23849	206	17	associates	associates	PROPN
brj-23849	206	18	,	,	PUNCT
brj-23849	206	19	montreal	montreal	PROPN
brj-23849	206	20	,	,	PUNCT
brj-23849	206	21	canada	canada	PROPN
brj-23849	206	22	,	,	PUNCT
brj-23849	206	23	pp	pp	X
brj-23849	206	24	.	.	PUNCT
brj-23849	207	1	2672	2672	NUM
brj-23849	207	2	-	-	SYM
brj-23849	207	3	2680	2680	NUM
brj-23849	207	4	.	.	PUNCT
brj-23849	208	1	doi	doi	NOUN
brj-23849	208	2	:	:	PUNCT
brj-23849	208	3	10.5555/2969033.2969125	10.5555/2969033.2969125	NUM
brj-23849	208	4	grant	grant	NOUN
brj-23849	208	5	-	-	PUNCT
brj-23849	208	6	jacob	jacob	PROPN
brj-23849	208	7	,	,	PUNCT
brj-23849	208	8	j.	j.	PROPN
brj-23849	208	9	a.	a.	PROPN
brj-23849	208	10	,	,	PUNCT
brj-23849	208	11	praeger	praeger	NOUN
brj-23849	208	12	,	,	PUNCT
brj-23849	208	13	m.	m.	NOUN
brj-23849	208	14	,	,	PUNCT
brj-23849	208	15	eason	eason	PROPN
brj-23849	208	16	,	,	PUNCT
brj-23849	208	17	r.	r.	PROPN
brj-23849	208	18	w.	w.	PROPN
brj-23849	208	19	,	,	PUNCT
brj-23849	208	20	and	and	CCONJ
brj-23849	208	21	mills	mill	NOUN
brj-23849	208	22	,	,	PUNCT
brj-23849	208	23	b.	b.	PROPN
brj-23849	208	24	(	(	PUNCT
brj-23849	208	25	2022	2022	NUM
brj-23849	208	26	)	)	PUNCT
brj-23849	208	27	.	.	PUNCT
brj-23849	209	1	“	"	PUNCT
brj-23849	209	2	generating	generate	VERB
brj-23849	209	3	images	image	NOUN
brj-23849	209	4	of	of	ADP
brj-23849	209	5	hydrated	hydrated	ADJ
brj-23849	209	6	pollen	pollen	NOUN
brj-23849	209	7	grains	grain	NOUN
brj-23849	209	8	using	use	VERB
brj-23849	209	9	deep	deep	ADJ
brj-23849	209	10	learning	learning	NOUN
brj-23849	209	11	,	,	PUNCT
brj-23849	209	12	”	"	PUNCT
brj-23849	209	13	iop	iop	PROPN
brj-23849	209	14	scinotes	scinote	VERB
brj-23849	209	15	3(2	3(2	NUM
brj-23849	209	16	)	)	PUNCT
brj-23849	209	17	,	,	PUNCT
brj-23849	209	18	article	article	NOUN
brj-23849	209	19	024001	024001	NUM
brj-23849	209	20	.	.	PUNCT
brj-23849	210	1	doi	doi	NOUN
brj-23849	210	2	:	:	PUNCT
brj-23849	210	3	10.1088/2633	10.1088/2633	NUM
brj-23849	210	4	-	-	SYM
brj-23849	210	5	1357	1357	NUM
brj-23849	210	6	/	/	SYM
brj-23849	210	7	ac6780	ac6780	NOUN
brj-23849	210	8	gu	gu	NOUN
brj-23849	210	9	,	,	PUNCT
brj-23849	210	10	x.	x.	PROPN
brj-23849	210	11	,	,	PUNCT
brj-23849	210	12	liu	liu	PROPN
brj-23849	210	13	,	,	PUNCT
brj-23849	210	14	j.	j.	PROPN
brj-23849	210	15	,	,	PUNCT
brj-23849	210	16	zou	zou	PROPN
brj-23849	210	17	,	,	PUNCT
brj-23849	210	18	x.	x.	NOUN
brj-23849	210	19	,	,	PUNCT
brj-23849	210	20	and	and	CCONJ
brj-23849	210	21	kuang	kuang	PROPN
brj-23849	210	22	,	,	PUNCT
brj-23849	210	23	p.	p.	NOUN
brj-23849	210	24	(	(	PUNCT
brj-23849	210	25	2017	2017	NUM
brj-23849	210	26	)	)	PUNCT
brj-23849	210	27	.	.	PUNCT
brj-23849	211	1	“	"	PUNCT
brj-23849	211	2	using	use	VERB
brj-23849	211	3	checkerboard	checkerboard	NOUN
brj-23849	211	4	rendering	rendering	NOUN
brj-23849	211	5	and	and	CCONJ
brj-23849	211	6	deconvolution	deconvolution	NOUN
brj-23849	211	7	to	to	PART
brj-23849	211	8	eliminate	eliminate	VERB
brj-23849	211	9	checkerboard	checkerboard	NOUN
brj-23849	211	10	artifacts	artifact	NOUN
brj-23849	211	11	in	in	ADP
brj-23849	211	12	images	image	NOUN
brj-23849	211	13	generated	generate	VERB
brj-23849	211	14	by	by	ADP
brj-23849	211	15	neural	neural	ADJ
brj-23849	211	16	networks	network	NOUN
brj-23849	211	17	,	,	PUNCT
brj-23849	211	18	”	"	PUNCT
brj-23849	211	19	in	in	ADP
brj-23849	211	20	:	:	PUNCT
brj-23849	211	21	proceedings	proceeding	NOUN
brj-23849	211	22	of	of	ADP
brj-23849	211	23	the	the	DET
brj-23849	211	24	14th	14th	ADJ
brj-23849	211	25	international	international	ADJ
brj-23849	211	26	computer	computer	NOUN
brj-23849	211	27	conference	conference	NOUN
brj-23849	211	28	on	on	ADP
brj-23849	211	29	wavelet	wavelet	NOUN
brj-23849	211	30	active	active	ADJ
brj-23849	211	31	media	medium	NOUN
brj-23849	211	32	technology	technology	NOUN
brj-23849	211	33	and	and	CCONJ
brj-23849	211	34	information	information	NOUN
brj-23849	211	35	processing	processing	NOUN
brj-23849	211	36	,	,	PUNCT
brj-23849	211	37	chengdu	chengdu	PROPN
brj-23849	211	38	,	,	PUNCT
brj-23849	211	39	china	china	PROPN
brj-23849	211	40	,	,	PUNCT
brj-23849	211	41	pp	pp	PROPN
brj-23849	211	42	.	.	PUNCT
brj-23849	211	43	197	197	NUM
brj-23849	211	44	-	-	SYM
brj-23849	211	45	200	200	NUM
brj-23849	211	46	.	.	PUNCT
brj-23849	212	1	doi	doi	NOUN
brj-23849	212	2	:	:	PUNCT
brj-23849	212	3	10.1109	10.1109	NUM
brj-23849	212	4	/	/	SYM
brj-23849	212	5	iccwamtip.2017.8301478	iccwamtip.2017.8301478	ADJ
brj-23849	212	6	isola	isola	PROPN
brj-23849	212	7	,	,	PUNCT
brj-23849	212	8	p.	p.	PROPN
brj-23849	212	9	,	,	PUNCT
brj-23849	212	10	zhu	zhu	PROPN
brj-23849	212	11	,	,	PUNCT
brj-23849	212	12	j.	j.	PROPN
brj-23849	212	13	y.	y.	PROPN
brj-23849	212	14	,	,	PUNCT
brj-23849	212	15	zhou	zhou	PROPN
brj-23849	212	16	,	,	PUNCT
brj-23849	212	17	t.	t.	PROPN
brj-23849	212	18	,	,	PUNCT
brj-23849	212	19	and	and	CCONJ
brj-23849	212	20	efros	efros	PROPN
brj-23849	212	21	,	,	PUNCT
brj-23849	212	22	a.	a.	NOUN
brj-23849	212	23	a.	a.	PROPN
brj-23849	213	1	(	(	PUNCT
brj-23849	213	2	2016	2016	NUM
brj-23849	213	3	)	)	PUNCT
brj-23849	213	4	.	.	PUNCT
brj-23849	214	1	“	"	PUNCT
brj-23849	214	2	image	image	NOUN
brj-23849	214	3	-	-	PUNCT
brj-23849	214	4	to	to	ADP
brj-23849	214	5	-	-	PUNCT
brj-23849	214	6	image	image	NOUN
brj-23849	214	7	translation	translation	NOUN
brj-23849	214	8	with	with	ADP
brj-23849	214	9	conditional	conditional	ADJ
brj-23849	214	10	adversarial	adversarial	ADJ
brj-23849	214	11	networks	network	NOUN
brj-23849	214	12	,	,	PUNCT
brj-23849	214	13	”	"	PUNCT
brj-23849	214	14	arxiv	arxiv	PROPN
brj-23849	214	15	1611.07004	1611.07004	PROPN
brj-23849	214	16	.	.	PUNCT
brj-23849	214	17	doi	doi	NOUN
brj-23849	214	18	:	:	PUNCT
brj-23849	214	19	10.48550	10.48550	NUM
brj-23849	214	20	/	/	SYM
brj-23849	214	21	arxiv.1611.07004	arxiv.1611.07004	X
brj-23849	214	22	karras	karra	NOUN
brj-23849	214	23	,	,	PUNCT
brj-23849	214	24	t.	t.	PROPN
brj-23849	214	25	,	,	PUNCT
brj-23849	214	26	aila	aila	PROPN
brj-23849	214	27	,	,	PUNCT
brj-23849	214	28	t.	t.	PROPN
brj-23849	214	29	,	,	PUNCT
brj-23849	214	30	laine	laine	PROPN
brj-23849	214	31	,	,	PUNCT
brj-23849	214	32	s.	s.	PROPN
brj-23849	214	33	,	,	PUNCT
brj-23849	214	34	and	and	CCONJ
brj-23849	214	35	lehtinen	lehtinen	PROPN
brj-23849	214	36	,	,	PUNCT
brj-23849	214	37	j.	j.	PROPN
brj-23849	214	38	(	(	PUNCT
brj-23849	214	39	2017	2017	NUM
brj-23849	214	40	)	)	PUNCT
brj-23849	214	41	.	.	PUNCT
brj-23849	215	1	“	"	PUNCT
brj-23849	215	2	progressive	progressive	ADJ
brj-23849	215	3	growing	grow	VERB
brj-23849	215	4	of	of	ADP
brj-23849	215	5	gans	gan	NOUN
brj-23849	215	6	for	for	ADP
brj-23849	215	7	improved	improved	ADJ
brj-23849	215	8	quality	quality	NOUN
brj-23849	215	9	,	,	PUNCT
brj-23849	215	10	stability	stability	NOUN
brj-23849	215	11	,	,	PUNCT
brj-23849	215	12	and	and	CCONJ
brj-23849	215	13	variation	variation	NOUN
brj-23849	215	14	,	,	PUNCT
brj-23849	215	15	”	"	PUNCT
brj-23849	215	16	arxiv	arxiv	PROPN
brj-23849	215	17	1710.1196	1710.1196	PROPN
brj-23849	215	18	.	.	PUNCT
brj-23849	216	1	doi	doi	NOUN
brj-23849	216	2	:	:	PUNCT
brj-23849	216	3	10.48550	10.48550	NUM
brj-23849	216	4	/	/	SYM
brj-23849	216	5	arxiv.1710.10196	arxiv.1710.10196	ADJ
brj-23849	216	6	kingma	kingma	PROPN
brj-23849	216	7	,	,	PUNCT
brj-23849	216	8	d.	d.	PROPN
brj-23849	216	9	,	,	PUNCT
brj-23849	216	10	and	and	CCONJ
brj-23849	216	11	ba	ba	PROPN
brj-23849	216	12	,	,	PUNCT
brj-23849	216	13	j.	j.	PROPN
brj-23849	216	14	(	(	PUNCT
brj-23849	216	15	2014	2014	NUM
brj-23849	216	16	)	)	PUNCT
brj-23849	216	17	.	.	PUNCT
brj-23849	217	1	“	"	PUNCT
brj-23849	217	2	adam	adam	PROPN
brj-23849	217	3	:	:	PUNCT
brj-23849	217	4	a	a	DET
brj-23849	217	5	method	method	NOUN
brj-23849	217	6	for	for	ADP
brj-23849	217	7	stochastic	stochastic	ADJ
brj-23849	217	8	optimization	optimization	NOUN
brj-23849	217	9	,	,	PUNCT
brj-23849	217	10	”	"	PUNCT
brj-23849	217	11	arxiv	arxiv	PROPN
brj-23849	217	12	1412.6980	1412.6980	PROPN
brj-23849	217	13	.	.	PUNCT
brj-23849	217	14	doi	doi	NOUN
brj-23849	217	15	:	:	PUNCT
brj-23849	217	16	10.48550	10.48550	NUM
brj-23849	217	17	/	/	SYM
brj-23849	217	18	arxiv.1412.6980	arxiv.1412.6980	PROPN
brj-23849	217	19	peerreviewed	peerreviewe	VERB
brj-23849	217	20	article	article	NOUN
brj-23849	217	21	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-23849	217	22	teng	teng	PROPN
brj-23849	217	23	et	et	PROPN
brj-23849	217	24	al	al	PROPN
brj-23849	217	25	.	.	PROPN
brj-23849	218	1	(	(	PUNCT
brj-23849	218	2	2024	2024	NUM
brj-23849	218	3	)	)	PUNCT
brj-23849	218	4	.	.	PUNCT
brj-23849	219	1	“	"	PUNCT
brj-23849	219	2	dcgan	dcgan	VERB
brj-23849	219	3	enhancement	enhancement	NOUN
brj-23849	219	4	method	method	NOUN
brj-23849	219	5	,	,	PUNCT
brj-23849	219	6	”	"	PUNCT
brj-23849	219	7	bioresources	bioresource	NOUN
brj-23849	219	8	19(4	19(4	NUM
brj-23849	219	9	)	)	PUNCT
brj-23849	219	10	,	,	PUNCT
brj-23849	219	11	9271	9271	NUM
brj-23849	219	12	-	-	SYM
brj-23849	219	13	9284	9284	NUM
brj-23849	219	14	.	.	PUNCT
brj-23849	220	1	9284	9284	NUM
brj-23849	220	2	liu	liu	PROPN
brj-23849	220	3	,	,	PUNCT
brj-23849	220	4	h.	h.	PROPN
brj-23849	220	5	,	,	PUNCT
brj-23849	220	6	luo	luo	PROPN
brj-23849	220	7	,	,	PUNCT
brj-23849	220	8	y.	y.	PROPN
brj-23849	220	9	,	,	PUNCT
brj-23849	220	10	wen	wen	PROPN
brj-23849	220	11	,	,	PUNCT
brj-23849	220	12	j.	j.	PROPN
brj-23849	220	13	,	,	PUNCT
brj-23849	220	14	zhang	zhang	PROPN
brj-23849	220	15	,	,	PUNCT
brj-23849	220	16	z.	z.	PROPN
brj-23849	220	17	,	,	PUNCT
brj-23849	220	18	feng	feng	PROPN
brj-23849	220	19	,	,	PUNCT
brj-23849	220	20	j.	j.	PROPN
brj-23849	220	21	,	,	PUNCT
brj-23849	220	22	and	and	CCONJ
brj-23849	220	23	tao	tao	PROPN
brj-23849	220	24	,	,	PUNCT
brj-23849	220	25	w.	w.	PROPN
brj-23849	220	26	(	(	PUNCT
brj-23849	220	27	2006	2006	NUM
brj-23849	220	28	)	)	PUNCT
brj-23849	220	29	.	.	PUNCT
brj-23849	221	1	“	"	PUNCT
brj-23849	221	2	pest	pest	VERB
brj-23849	221	3	risk	risk	NOUN
brj-23849	221	4	assessment	assessment	NOUN
brj-23849	221	5	of	of	ADP
brj-23849	221	6	dendroctonus	dendroctonus	ADJ
brj-23849	221	7	valens	valen	NOUN
brj-23849	221	8	,	,	PUNCT
brj-23849	221	9	hyphantria	hyphantria	PROPN
brj-23849	221	10	cunea	cunea	PROPN
brj-23849	221	11	,	,	PUNCT
brj-23849	221	12	and	and	CCONJ
brj-23849	221	13	apriona	apriona	NOUN
brj-23849	221	14	swainsoni	swainsoni	NOUN
brj-23849	221	15	in	in	ADP
brj-23849	221	16	beijing	beijing	PROPN
brj-23849	221	17	,	,	PUNCT
brj-23849	221	18	”	"	PUNCT
brj-23849	221	19	frontiers	frontier	NOUN
brj-23849	221	20	of	of	ADP
brj-23849	221	21	forestry	forestry	NOUN
brj-23849	221	22	in	in	ADP
brj-23849	221	23	china	china	PROPN
brj-23849	221	24	1(3	1(3	NUM
brj-23849	221	25	)	)	PUNCT
brj-23849	221	26	,	,	PUNCT
brj-23849	221	27	328	328	NUM
brj-23849	221	28	-	-	SYM
brj-23849	221	29	335	335	NUM
brj-23849	221	30	.	.	PUNCT
brj-23849	222	1	doi	doi	NOUN
brj-23849	222	2	:	:	PUNCT
brj-23849	222	3	10.1007	10.1007	NUM
brj-23849	222	4	/	/	SYM
brj-23849	222	5	s11461	s11461	NOUN
brj-23849	222	6	-	-	NOUN
brj-23849	222	7	006	006	NUM
brj-23849	222	8	-	-	PUNCT
brj-23849	222	9	0025	0025	NUM
brj-23849	222	10	-	-	PUNCT
brj-23849	222	11	5	5	NUM
brj-23849	222	12	lopes	lope	NOUN
brj-23849	222	13	,	,	PUNCT
brj-23849	222	14	a.	a.	PROPN
brj-23849	222	15	t.	t.	PROPN
brj-23849	222	16	,	,	PUNCT
brj-23849	222	17	de	de	X
brj-23849	222	18	aguiar	aguiar	PROPN
brj-23849	222	19	,	,	PUNCT
brj-23849	222	20	e.	e.	PROPN
brj-23849	222	21	,	,	PUNCT
brj-23849	222	22	de	de	PROPN
brj-23849	222	23	souza	souza	PROPN
brj-23849	222	24	,	,	PUNCT
brj-23849	222	25	a.	a.	PROPN
brj-23849	222	26	f.	f.	PROPN
brj-23849	222	27	,	,	PUNCT
brj-23849	222	28	and	and	CCONJ
brj-23849	222	29	oliveira	oliveira	PROPN
brj-23849	222	30	-	-	PUNCT
brj-23849	222	31	santos	santos	PROPN
brj-23849	222	32	,	,	PUNCT
brj-23849	222	33	t.	t.	NOUN
brj-23849	222	34	(	(	PUNCT
brj-23849	222	35	2017	2017	NUM
brj-23849	222	36	)	)	PUNCT
brj-23849	222	37	.	.	PUNCT
brj-23849	223	1	“	"	PUNCT
brj-23849	223	2	facial	facial	ADJ
brj-23849	223	3	expression	expression	NOUN
brj-23849	223	4	recognition	recognition	NOUN
brj-23849	223	5	with	with	ADP
brj-23849	223	6	convolutional	convolutional	ADJ
brj-23849	223	7	neural	neural	ADJ
brj-23849	223	8	networks	network	NOUN
brj-23849	223	9	:	:	PUNCT
brj-23849	223	10	coping	cope	VERB
brj-23849	223	11	with	with	ADP
brj-23849	223	12	few	few	ADJ
brj-23849	223	13	data	datum	NOUN
brj-23849	223	14	and	and	CCONJ
brj-23849	223	15	the	the	DET
brj-23849	223	16	training	training	NOUN
brj-23849	223	17	sample	sample	NOUN
brj-23849	223	18	order	order	NOUN
brj-23849	223	19	,	,	PUNCT
brj-23849	223	20	”	"	PUNCT
brj-23849	223	21	pattern	pattern	NOUN
brj-23849	223	22	recognition	recognition	NOUN
brj-23849	223	23	61	61	NUM
brj-23849	223	24	,	,	PUNCT
brj-23849	223	25	610	610	NUM
brj-23849	223	26	-	-	SYM
brj-23849	223	27	628	628	NUM
brj-23849	223	28	.	.	PUNCT
brj-23849	224	1	doi	doi	NOUN
brj-23849	224	2	:	:	PUNCT
brj-23849	224	3	10.1016	10.1016	NUM
brj-23849	224	4	/	/	SYM
brj-23849	224	5	j.patcog.2016.07.026	j.patcog.2016.07.026	PROPN
brj-23849	224	6	radford	radford	PROPN
brj-23849	224	7	,	,	PUNCT
brj-23849	224	8	a.	a.	PROPN
brj-23849	224	9	,	,	PUNCT
brj-23849	224	10	metz	metz	PROPN
brj-23849	224	11	,	,	PUNCT
brj-23849	224	12	l.	l.	PROPN
brj-23849	224	13	,	,	PUNCT
brj-23849	224	14	and	and	CCONJ
brj-23849	224	15	chintala	chintala	PROPN
brj-23849	224	16	,	,	PUNCT
brj-23849	224	17	s.	s.	PROPN
brj-23849	224	18	(	(	PUNCT
brj-23849	224	19	2015	2015	NUM
brj-23849	224	20	)	)	PUNCT
brj-23849	224	21	.	.	PUNCT
brj-23849	225	1	“	"	PUNCT
brj-23849	225	2	unsupervised	unsupervised	ADJ
brj-23849	225	3	representation	representation	NOUN
brj-23849	225	4	learning	learn	VERB
brj-23849	225	5	with	with	ADP
brj-23849	225	6	deep	deep	ADJ
brj-23849	225	7	convolutional	convolutional	ADJ
brj-23849	225	8	generative	generative	ADJ
brj-23849	225	9	adversarial	adversarial	ADJ
brj-23849	225	10	networks	network	NOUN
brj-23849	225	11	,	,	PUNCT
brj-23849	225	12	”	"	PUNCT
brj-23849	225	13	arxiv	arxiv	PROPN
brj-23849	225	14	1511.06434	1511.06434	NUM
brj-23849	225	15	.	.	PUNCT
brj-23849	226	1	doi	doi	NOUN
brj-23849	226	2	:	:	PUNCT
brj-23849	226	3	10.48550	10.48550	NUM
brj-23849	226	4	/	/	SYM
brj-23849	226	5	arxiv.1511.06434	arxiv.1511.06434	PRON
brj-23849	227	1	srivastava	srivastava	PROPN
brj-23849	227	2	,	,	PUNCT
brj-23849	227	3	n.	n.	PROPN
brj-23849	227	4	,	,	PUNCT
brj-23849	227	5	hinton	hinton	PROPN
brj-23849	227	6	,	,	PUNCT
brj-23849	227	7	g.	g.	PROPN
brj-23849	227	8	,	,	PUNCT
brj-23849	227	9	krizhevsky	krizhevsky	PROPN
brj-23849	227	10	,	,	PUNCT
brj-23849	227	11	a.	a.	NOUN
brj-23849	227	12	,	,	PUNCT
brj-23849	227	13	sutskever	sutskever	PROPN
brj-23849	227	14	,	,	PUNCT
brj-23849	227	15	i.	i.	PROPN
brj-23849	227	16	,	,	PUNCT
brj-23849	227	17	and	and	CCONJ
brj-23849	227	18	salakhutdinov	salakhutdinov	PROPN
brj-23849	227	19	,	,	PUNCT
brj-23849	227	20	r.	r.	PROPN
brj-23849	227	21	(	(	PUNCT
brj-23849	227	22	2014	2014	NUM
brj-23849	227	23	)	)	PUNCT
brj-23849	227	24	.	.	PUNCT
brj-23849	228	1	“	"	PUNCT
brj-23849	228	2	dropout	dropout	NOUN
brj-23849	228	3	:	:	PUNCT
brj-23849	228	4	a	a	DET
brj-23849	228	5	simple	simple	ADJ
brj-23849	228	6	way	way	NOUN
brj-23849	228	7	to	to	PART
brj-23849	228	8	prevent	prevent	VERB
brj-23849	228	9	neural	neural	ADJ
brj-23849	228	10	networks	network	NOUN
brj-23849	228	11	from	from	ADP
brj-23849	228	12	overfitting	overfitte	VERB
brj-23849	228	13	,	,	PUNCT
brj-23849	228	14	”	"	PUNCT
brj-23849	228	15	journal	journal	NOUN
brj-23849	228	16	of	of	ADP
brj-23849	228	17	machine	machine	NOUN
brj-23849	228	18	learning	learn	VERB
brj-23849	228	19	research	research	NOUN
brj-23849	228	20	15(1	15(1	NUM
brj-23849	228	21	)	)	PUNCT
brj-23849	228	22	,	,	PUNCT
brj-23849	228	23	1929	1929	NUM
brj-23849	228	24	-	-	SYM
brj-23849	228	25	1958	1958	NUM
brj-23849	228	26	.	.	PUNCT
brj-23849	229	1	doi	doi	NOUN
brj-23849	229	2	:	:	PUNCT
brj-23849	229	3	10.5555/2627435.2670313	10.5555/2627435.2670313	NUM
brj-23849	229	4	sugawara	sugawara	NOUN
brj-23849	229	5	,	,	PUNCT
brj-23849	229	6	y.	y.	PROPN
brj-23849	229	7	,	,	PUNCT
brj-23849	229	8	shiota	shiota	NOUN
brj-23849	229	9	,	,	PUNCT
brj-23849	229	10	s.	s.	PROPN
brj-23849	229	11	,	,	PUNCT
brj-23849	229	12	and	and	CCONJ
brj-23849	229	13	kiya	kiya	PROPN
brj-23849	229	14	,	,	PUNCT
brj-23849	229	15	h.	h.	PROPN
brj-23849	229	16	(	(	PUNCT
brj-23849	229	17	2019	2019	NUM
brj-23849	229	18	)	)	PUNCT
brj-23849	229	19	.	.	PUNCT
brj-23849	230	1	“	"	PUNCT
brj-23849	230	2	checkerboard	checkerboard	NOUN
brj-23849	230	3	artifacts	artifact	NOUN
brj-23849	230	4	free	free	ADJ
brj-23849	230	5	convolutional	convolutional	ADJ
brj-23849	230	6	neural	neural	ADJ
brj-23849	230	7	networks	network	NOUN
brj-23849	230	8	,	,	PUNCT
brj-23849	230	9	”	"	PUNCT
brj-23849	230	10	apsipa	apsipa	ADJ
brj-23849	230	11	transactions	transaction	NOUN
brj-23849	230	12	on	on	ADP
brj-23849	230	13	signal	signal	NOUN
brj-23849	230	14	and	and	CCONJ
brj-23849	230	15	information	information	NOUN
brj-23849	230	16	processing	processing	NOUN
brj-23849	230	17	8	8	NUM
brj-23849	230	18	,	,	PUNCT
brj-23849	230	19	1	1	NUM
brj-23849	230	20	-	-	SYM
brj-23849	230	21	9	9	NUM
brj-23849	230	22	.	.	PUNCT
brj-23849	230	23	doi	doi	NOUN
brj-23849	230	24	:	:	PUNCT
brj-23849	230	25	10.1017	10.1017	NUM
brj-23849	230	26	/	/	SYM
brj-23849	230	27	atsip.2019.2	atsip.2019.2	NOUN
brj-23849	230	28	sun	sun	NOUN
brj-23849	230	29	,	,	PUNCT
brj-23849	230	30	x.	x.	PROPN
brj-23849	230	31	,	,	PUNCT
brj-23849	230	32	lv	lv	PROPN
brj-23849	230	33	,	,	PUNCT
brj-23849	230	34	m.	m.	NOUN
brj-23849	230	35	,	,	PUNCT
brj-23849	230	36	quan	quan	PROPN
brj-23849	230	37	,	,	PUNCT
brj-23849	230	38	c.	c.	PROPN
brj-23849	230	39	,	,	PUNCT
brj-23849	230	40	and	and	CCONJ
brj-23849	230	41	ren	ren	PROPN
brj-23849	230	42	,	,	PUNCT
brj-23849	230	43	f.	f.	PROPN
brj-23849	230	44	(	(	PUNCT
brj-23849	230	45	2017	2017	NUM
brj-23849	230	46	)	)	PUNCT
brj-23849	230	47	.	.	PUNCT
brj-23849	231	1	“	"	PUNCT
brj-23849	231	2	improved	improve	VERB
brj-23849	231	3	facial	facial	ADJ
brj-23849	231	4	expression	expression	NOUN
brj-23849	231	5	recognition	recognition	NOUN
brj-23849	231	6	method	method	NOUN
brj-23849	231	7	based	base	VERB
brj-23849	231	8	on	on	ADP
brj-23849	231	9	roi	roi	NOUN
brj-23849	231	10	deep	deep	ADJ
brj-23849	231	11	convolutional	convolutional	ADJ
brj-23849	231	12	neural	neural	ADJ
brj-23849	231	13	network	network	NOUN
brj-23849	231	14	,	,	PUNCT
brj-23849	231	15	”	"	PUNCT
brj-23849	231	16	in	in	ADP
brj-23849	231	17	:	:	PUNCT
brj-23849	231	18	proceedings	proceeding	NOUN
brj-23849	231	19	of	of	ADP
brj-23849	231	20	the	the	DET
brj-23849	231	21	7th	7th	ADJ
brj-23849	231	22	international	international	ADJ
brj-23849	231	23	conference	conference	NOUN
brj-23849	231	24	on	on	ADP
brj-23849	231	25	affective	affective	ADJ
brj-23849	231	26	computing	computing	NOUN
brj-23849	231	27	and	and	CCONJ
brj-23849	231	28	intelligent	intelligent	ADJ
brj-23849	231	29	interaction	interaction	NOUN
brj-23849	231	30	,	,	PUNCT
brj-23849	231	31	san	san	PROPN
brj-23849	231	32	antonio	antonio	PROPN
brj-23849	231	33	,	,	PUNCT
brj-23849	231	34	tx	tx	PROPN
brj-23849	231	35	,	,	PUNCT
brj-23849	231	36	usa	usa	PROPN
brj-23849	231	37	,	,	PUNCT
brj-23849	231	38	ieee	ieee	NOUN
brj-23849	231	39	computer	computer	NOUN
brj-23849	231	40	society	society	NOUN
brj-23849	231	41	,	,	PUNCT
brj-23849	231	42	pp	pp	ADP
brj-23849	231	43	.	.	PUNCT
brj-23849	232	1	256	256	NUM
brj-23849	232	2	-	-	SYM
brj-23849	232	3	261	261	NUM
brj-23849	232	4	.	.	PUNCT
brj-23849	232	5	doi	doi	NOUN
brj-23849	232	6	:	:	PUNCT
brj-23849	232	7	10.1109	10.1109	NUM
brj-23849	232	8	/	/	SYM
brj-23849	232	9	acii.2017.8273609	acii.2017.8273609	PROPN
brj-23849	232	10	yamamoto	yamamoto	NOUN
brj-23849	232	11	,	,	PUNCT
brj-23849	232	12	r.	r.	PROPN
brj-23849	232	13	,	,	PUNCT
brj-23849	232	14	song	song	NOUN
brj-23849	232	15	,	,	PUNCT
brj-23849	232	16	e.	e.	PROPN
brj-23849	232	17	,	,	PUNCT
brj-23849	232	18	and	and	CCONJ
brj-23849	232	19	kim	kim	PROPN
brj-23849	232	20	,	,	PUNCT
brj-23849	232	21	j.	j.	PROPN
brj-23849	232	22	m.	m.	PROPN
brj-23849	232	23	(	(	PUNCT
brj-23849	232	24	2019	2019	NUM
brj-23849	232	25	)	)	PUNCT
brj-23849	232	26	.	.	PUNCT
brj-23849	233	1	“	"	PUNCT
brj-23849	233	2	parallel	parallel	ADJ
brj-23849	233	3	wavegan	wavegan	NOUN
brj-23849	233	4	:	:	PUNCT
brj-23849	233	5	a	a	DET
brj-23849	233	6	fast	fast	ADJ
brj-23849	233	7	waveform	waveform	NOUN
brj-23849	233	8	generation	generation	NOUN
brj-23849	233	9	model	model	NOUN
brj-23849	233	10	based	base	VERB
brj-23849	233	11	on	on	ADP
brj-23849	233	12	generative	generative	ADJ
brj-23849	233	13	adversarial	adversarial	ADJ
brj-23849	233	14	networks	network	NOUN
brj-23849	233	15	with	with	ADP
brj-23849	233	16	a	a	DET
brj-23849	233	17	multi	multi	ADJ
brj-23849	233	18	-	-	ADJ
brj-23849	233	19	resolution	resolution	ADJ
brj-23849	233	20	spectrogram	spectrogram	NOUN
brj-23849	233	21	,	,	PUNCT
brj-23849	233	22	”	"	PUNCT
brj-23849	233	23	in	in	ADP
brj-23849	233	24	:	:	PUNCT
brj-23849	233	25	proceedings	proceeding	NOUN
brj-23849	233	26	of	of	ADP
brj-23849	233	27	the	the	DET
brj-23849	233	28	international	international	ADJ
brj-23849	233	29	conference	conference	NOUN
brj-23849	233	30	on	on	ADP
brj-23849	233	31	acoustics	acoustic	NOUN
brj-23849	233	32	,	,	PUNCT
brj-23849	233	33	speech	speech	NOUN
brj-23849	233	34	,	,	PUNCT
brj-23849	233	35	and	and	CCONJ
brj-23849	233	36	signal	signal	NOUN
brj-23849	233	37	processing	processing	NOUN
brj-23849	233	38	,	,	PUNCT
brj-23849	233	39	brighton	brighton	PROPN
brj-23849	233	40	,	,	PUNCT
brj-23849	233	41	uk	uk	PROPN
brj-23849	233	42	.	.	PROPN
brj-23849	233	43	doi	doi	PROPN
brj-23849	233	44	:	:	PUNCT
brj-23849	233	45	10.48550	10.48550	NUM
brj-23849	233	46	/	/	SYM
brj-23849	233	47	arxiv.1910.11480	arxiv.1910.11480	PROPN
brj-23849	233	48	yang	yang	PROPN
brj-23849	233	49	,	,	PUNCT
brj-23849	233	50	r.	r.	PROPN
brj-23849	233	51	,	,	PUNCT
brj-23849	233	52	wang	wang	PROPN
brj-23849	233	53	,	,	PUNCT
brj-23849	233	54	y.	y.	PROPN
brj-23849	233	55	,	,	PUNCT
brj-23849	233	56	xu	xu	PROPN
brj-23849	233	57	,	,	PUNCT
brj-23849	233	58	y.	y.	PROPN
brj-23849	233	59	,	,	PUNCT
brj-23849	233	60	and	and	CCONJ
brj-23849	233	61	zhang	zhang	PROPN
brj-23849	233	62	,	,	PUNCT
brj-23849	233	63	m.	m.	NOUN
brj-23849	233	64	(	(	PUNCT
brj-23849	233	65	2022	2022	NUM
brj-23849	233	66	)	)	PUNCT
brj-23849	233	67	.	.	PUNCT
brj-23849	234	1	“	"	PUNCT
brj-23849	234	2	application	application	NOUN
brj-23849	234	3	of	of	ADP
brj-23849	234	4	neural	neural	ADJ
brj-23849	234	5	network	network	NOUN
brj-23849	234	6	in	in	ADP
brj-23849	234	7	pixel	pixel	PROPN
brj-23849	234	8	art	art	NOUN
brj-23849	234	9	creation	creation	NOUN
brj-23849	234	10	:	:	PUNCT
brj-23849	234	11	bi	bi	ADJ
brj-23849	234	12	-	-	ADJ
brj-23849	234	13	directional	directional	ADJ
brj-23849	234	14	conversion	conversion	NOUN
brj-23849	234	15	between	between	ADP
brj-23849	234	16	photo	photo	NOUN
brj-23849	234	17	and	and	CCONJ
brj-23849	234	18	pixel	pixel	ADJ
brj-23849	234	19	art	art	NOUN
brj-23849	234	20	with	with	ADP
brj-23849	234	21	gan	gan	PROPN
brj-23849	234	22	base	base	NOUN
brj-23849	234	23	model	model	NOUN
brj-23849	234	24	,	,	PUNCT
brj-23849	234	25	”	"	PUNCT
brj-23849	234	26	in	in	ADP
brj-23849	234	27	:	:	PUNCT
brj-23849	234	28	proceedings	proceeding	NOUN
brj-23849	234	29	of	of	ADP
brj-23849	234	30	the	the	DET
brj-23849	234	31	2nd	2nd	ADJ
brj-23849	234	32	international	international	ADJ
brj-23849	234	33	conference	conference	NOUN
brj-23849	234	34	on	on	ADP
brj-23849	234	35	consumer	consumer	NOUN
brj-23849	234	36	electronics	electronic	NOUN
brj-23849	234	37	and	and	CCONJ
brj-23849	234	38	computer	computer	NOUN
brj-23849	234	39	engineering	engineering	NOUN
brj-23849	234	40	,	,	PUNCT
brj-23849	234	41	guangzhou	guangzhou	PROPN
brj-23849	234	42	,	,	PUNCT
brj-23849	234	43	china	china	PROPN
brj-23849	234	44	,	,	PUNCT
brj-23849	234	45	ieee	ieee	PROPN
brj-23849	234	46	,	,	PUNCT
brj-23849	234	47	pp	pp	ADJ
brj-23849	234	48	.	.	PUNCT
brj-23849	235	1	14	14	NUM
brj-23849	235	2	-	-	SYM
brj-23849	235	3	16	16	NUM
brj-23849	235	4	.	.	PUNCT
brj-23849	236	1	doi	doi	NOUN
brj-23849	236	2	:	:	PUNCT
brj-23849	236	3	10.1109	10.1109	NUM
brj-23849	236	4	/	/	SYM
brj-23849	236	5	iccece54139.2022.9712735	iccece54139.2022.9712735	PROPN
brj-23849	236	6	yang	yang	PROPN
brj-23849	236	7	,	,	PUNCT
brj-23849	236	8	y.	y.	PROPN
brj-23849	236	9	,	,	PUNCT
brj-23849	236	10	and	and	CCONJ
brj-23849	236	11	li	li	PROPN
brj-23849	236	12	,	,	PUNCT
brj-23849	236	13	c.	c.	PROPN
brj-23849	236	14	(	(	PUNCT
brj-23849	236	15	2021	2021	NUM
brj-23849	236	16	)	)	PUNCT
brj-23849	236	17	.	.	PUNCT
brj-23849	237	1	“	"	PUNCT
brj-23849	237	2	quantitative	quantitative	ADJ
brj-23849	237	3	analysis	analysis	NOUN
brj-23849	237	4	of	of	ADP
brj-23849	237	5	the	the	DET
brj-23849	237	6	generalization	generalization	NOUN
brj-23849	237	7	ability	ability	NOUN
brj-23849	237	8	of	of	ADP
brj-23849	237	9	deep	deep	ADJ
brj-23849	237	10	feedforward	feedforward	ADJ
brj-23849	237	11	neural	neural	ADJ
brj-23849	237	12	networks	network	NOUN
brj-23849	237	13	,	,	PUNCT
brj-23849	237	14	”	"	PUNCT
brj-23849	237	15	journal	journal	NOUN
brj-23849	237	16	of	of	ADP
brj-23849	237	17	intelligent	intelligent	ADJ
brj-23849	237	18	and	and	CCONJ
brj-23849	237	19	fuzzy	fuzzy	ADJ
brj-23849	237	20	systems	system	NOUN
brj-23849	237	21	40(3	40(3	NOUN
brj-23849	237	22	)	)	PUNCT
brj-23849	237	23	,	,	PUNCT
brj-23849	237	24	48674876	48674876	NUM
brj-23849	237	25	.	.	PUNCT
brj-23849	238	1	doi	doi	NOUN
brj-23849	238	2	:	:	PUNCT
brj-23849	238	3	10.3233	10.3233	NUM
brj-23849	238	4	/	/	SYM
brj-23849	238	5	jifs-201679	jifs-201679	PROPN
brj-23849	238	6	yang	yang	PROPN
brj-23849	238	7	,	,	PUNCT
brj-23849	238	8	z.	z.	PROPN
brj-23849	238	9	q.	q.	PROPN
brj-23849	238	10	,	,	PUNCT
brj-23849	238	11	wang	wang	PROPN
brj-23849	238	12	,	,	PUNCT
brj-23849	238	13	x.	x.	PROPN
brj-23849	238	14	y.	y.	PROPN
brj-23849	238	15	,	,	PUNCT
brj-23849	238	16	wei	wei	PROPN
brj-23849	238	17	,	,	PUNCT
brj-23849	238	18	j.	j.	PROPN
brj-23849	238	19	r.	r.	PROPN
brj-23849	238	20	,	,	PUNCT
brj-23849	238	21	qu	qu	PROPN
brj-23849	238	22	,	,	PUNCT
brj-23849	238	23	h.	h.	PROPN
brj-23849	238	24	r.	r.	PROPN
brj-23849	238	25	,	,	PUNCT
brj-23849	238	26	and	and	CCONJ
brj-23849	238	27	qiao	qiao	PROPN
brj-23849	238	28	,	,	PUNCT
brj-23849	238	29	x.	x.	PROPN
brj-23849	238	30	r.	r.	PROPN
brj-23849	238	31	(	(	PUNCT
brj-23849	238	32	2008	2008	NUM
brj-23849	238	33	)	)	PUNCT
brj-23849	238	34	.	.	PUNCT
brj-23849	239	1	“	"	PUNCT
brj-23849	239	2	survey	survey	NOUN
brj-23849	239	3	of	of	ADP
brj-23849	239	4	the	the	DET
brj-23849	239	5	native	native	ADJ
brj-23849	239	6	insect	insect	NOUN
brj-23849	239	7	natural	natural	ADJ
brj-23849	239	8	enemies	enemy	NOUN
brj-23849	239	9	of	of	ADP
brj-23849	239	10	hyphantria	hyphantria	PROPN
brj-23849	239	11	cunea	cunea	PROPN
brj-23849	239	12	(	(	PUNCT
brj-23849	239	13	drury	drury	PROPN
brj-23849	239	14	)	)	PUNCT
brj-23849	239	15	(	(	PUNCT
brj-23849	239	16	lepidoptera	lepidoptera	NOUN
brj-23849	239	17	:	:	PUNCT
brj-23849	239	18	arctiidae	arctiidae	NOUN
brj-23849	239	19	)	)	PUNCT
brj-23849	239	20	in	in	ADP
brj-23849	239	21	china	china	PROPN
brj-23849	239	22	,	,	PUNCT
brj-23849	239	23	”	"	PUNCT
brj-23849	239	24	bulletin	bulletin	NOUN
brj-23849	239	25	of	of	ADP
brj-23849	239	26	entomological	entomological	ADJ
brj-23849	239	27	research	research	NOUN
brj-23849	239	28	98(3	98(3	PROPN
brj-23849	239	29	)	)	PUNCT
brj-23849	239	30	,	,	PUNCT
brj-23849	239	31	293	293	NUM
brj-23849	239	32	-	-	SYM
brj-23849	239	33	302	302	NUM
brj-23849	239	34	.	.	PUNCT
brj-23849	240	1	doi	doi	NOUN
brj-23849	240	2	:	:	PUNCT
brj-23849	240	3	10.1017	10.1017	NUM
brj-23849	240	4	/	/	SYM
brj-23849	240	5	s0007485308005609	s0007485308005609	PROPN
brj-23849	240	6	zhou	zhou	NOUN
brj-23849	240	7	,	,	PUNCT
brj-23849	240	8	z.-h	z.-h	NOUN
brj-23849	240	9	.	.	PUNCT
brj-23849	240	10	,	,	PUNCT
brj-23849	240	11	and	and	CCONJ
brj-23849	240	12	jiang	jiang	PROPN
brj-23849	240	13	,	,	PUNCT
brj-23849	240	14	y.	y.	PROPN
brj-23849	240	15	(	(	PUNCT
brj-23849	240	16	2004	2004	NUM
brj-23849	240	17	)	)	PUNCT
brj-23849	240	18	.	.	PUNCT
brj-23849	241	1	“	"	PUNCT
brj-23849	241	2	nec4.5	nec4.5	PROPN
brj-23849	241	3	:	:	PUNCT
brj-23849	241	4	neural	neural	ADJ
brj-23849	241	5	ensemble	ensemble	ADJ
brj-23849	241	6	based	base	VERB
brj-23849	241	7	c4.5	c4.5	PROPN
brj-23849	241	8	,	,	PUNCT
brj-23849	241	9	”	"	PUNCT
brj-23849	241	10	ieee	ieee	NOUN
brj-23849	241	11	transactions	transaction	NOUN
brj-23849	241	12	on	on	ADP
brj-23849	241	13	knowledge	knowledge	NOUN
brj-23849	241	14	and	and	CCONJ
brj-23849	241	15	data	datum	NOUN
brj-23849	241	16	engineering	engineering	NOUN
brj-23849	241	17	16(6	16(6	NUM
brj-23849	241	18	)	)	PUNCT
brj-23849	241	19	,	,	PUNCT
brj-23849	241	20	770	770	NUM
brj-23849	241	21	-	-	SYM
brj-23849	241	22	773	773	NUM
brj-23849	241	23	.	.	PUNCT
brj-23849	242	1	doi	doi	NOUN
brj-23849	242	2	:	:	PUNCT
brj-23849	242	3	10.1109	10.1109	NUM
brj-23849	242	4	/	/	SYM
brj-23849	242	5	tkde.2004.11	tkde.2004.11	NOUN
brj-23849	242	6	article	article	NOUN
brj-23849	242	7	submitted	submit	VERB
brj-23849	242	8	:	:	PUNCT
brj-23849	242	9	july	july	PROPN
brj-23849	242	10	30	30	NUM
brj-23849	242	11	,	,	PUNCT
brj-23849	242	12	2024	2024	NUM
brj-23849	242	13	;	;	PUNCT
brj-23849	242	14	peer	peer	NOUN
brj-23849	242	15	review	review	NOUN
brj-23849	242	16	completed	complete	VERB
brj-23849	242	17	:	:	PUNCT
brj-23849	242	18	august	august	PROPN
brj-23849	242	19	31	31	NUM
brj-23849	242	20	,	,	PUNCT
brj-23849	242	21	2024	2024	NUM
brj-23849	242	22	;	;	PUNCT
brj-23849	242	23	revised	revise	VERB
brj-23849	242	24	version	version	NOUN
brj-23849	242	25	received	receive	VERB
brj-23849	242	26	:	:	PUNCT
brj-23849	242	27	september	september	PROPN
brj-23849	242	28	26	26	NUM
brj-23849	242	29	,	,	PUNCT
brj-23849	242	30	2024	2024	NUM
brj-23849	242	31	;	;	PUNCT
brj-23849	242	32	accepted	accept	VERB
brj-23849	242	33	:	:	PUNCT
brj-23849	242	34	september	september	PROPN
brj-23849	242	35	27	27	NUM
brj-23849	242	36	,	,	PUNCT
brj-23849	242	37	2024	2024	NUM
brj-23849	242	38	;	;	PUNCT
brj-23849	242	39	published	publish	VERB
brj-23849	242	40	:	:	PUNCT
brj-23849	242	41	october	october	PROPN
brj-23849	242	42	16	16	NUM
brj-23849	242	43	,	,	PUNCT
brj-23849	242	44	2024	2024	NUM
brj-23849	242	45	.	.	PUNCT
brj-23849	243	1	doi	doi	NOUN
brj-23849	243	2	:	:	PUNCT
brj-23849	243	3	10.15376	10.15376	NUM
brj-23849	243	4	/	/	SYM
brj-23849	243	5	biores.19.4.9271	biores.19.4.9271	PROPN
brj-23849	243	6	-	-	SYM
brj-23849	243	7	9284	9284	NUM