{"id":"https://openalex.org/W4297094705","doi":"https://doi.org/10.1109/access.2022.3210130","title":"Stochastically Flipping Labels of Discriminator\u2019s Outputs for Training Generative Adversarial Networks","display_name":"Stochastically Flipping Labels of Discriminator\u2019s Outputs for Training Generative Adversarial Networks","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4297094705","doi":"https://doi.org/10.1109/access.2022.3210130"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3210130","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3210130","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09903615.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09903615.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Rui Yang","orcid":"https://orcid.org/0000-0001-6097-418X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Rui Yang","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-6097-418X","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043253670","display_name":"Duc Minh Vo","orcid":"https://orcid.org/0000-0003-4839-032X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Duc Minh Vo","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0003-4839-032X","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050229964","display_name":"Hideki Nakayama","orcid":"https://orcid.org/0000-0001-8726-2780"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideki Nakayama","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8726-2780","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0813,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.33916755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":null,"first_page":"103644","last_page":"103654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.9396065473556519},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8368269205093384},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7637183666229248},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6159114241600037},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5779169201850891},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5139026641845703},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5079470276832581},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41665732860565186},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36686283349990845},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3385384678840637},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.170196533203125},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1341889202594757}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.9396065473556519},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8368269205093384},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7637183666229248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6159114241600037},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5779169201850891},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5139026641845703},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5079470276832581},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41665732860565186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36686283349990845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3385384678840637},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.170196533203125},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1341889202594757},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3210130","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3210130","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09903615.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c0356fd6922e4b258be249ca131cc833","is_oa":true,"landing_page_url":"https://doaj.org/article/c0356fd6922e4b258be249ca131cc833","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 103644-103654 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3210130","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3210130","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09903615.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7599999904632568}],"awards":[{"id":"https://openalex.org/G1681524166","display_name":null,"funder_award_id":"JP22K17947","funder_id":"https://openalex.org/F4320322832","funder_display_name":"University of Tokyo"},{"id":"https://openalex.org/G5912977134","display_name":"Building World Knowledge by Grounding Language and Multimedia","funder_award_id":"19H04166","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G657792233","display_name":"Vision and language cross-modal for training conditional GANs with long-tail data.","funder_award_id":"22K17947","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G668487432","display_name":null,"funder_award_id":"JP22H05015","funder_id":"https://openalex.org/F4320322832","funder_display_name":"University of Tokyo"},{"id":"https://openalex.org/G7753977249","display_name":"Revisiting visual communication via characters","funder_award_id":"22H00540","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G8358115428","display_name":null,"funder_award_id":"JP19H04166","funder_id":"https://openalex.org/F4320322832","funder_display_name":"University of Tokyo"}],"funders":[{"id":"https://openalex.org/F4320322832","display_name":"University of Tokyo","ror":"https://ror.org/057zh3y96"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4297094705.pdf","grobid_xml":"https://content.openalex.org/works/W4297094705.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W930928758","https://openalex.org/W1503425191","https://openalex.org/W1522301498","https://openalex.org/W1834627138","https://openalex.org/W1959608418","https://openalex.org/W2095705004","https://openalex.org/W2108598243","https://openalex.org/W2551176409","https://openalex.org/W2577946330","https://openalex.org/W2751565964","https://openalex.org/W2765407302","https://openalex.org/W2798405286","https://openalex.org/W2948697666","https://openalex.org/W2962770929","https://openalex.org/W2963073614","https://openalex.org/W2963185411","https://openalex.org/W2964201867","https://openalex.org/W2970636982","https://openalex.org/W2987281104","https://openalex.org/W2990138404","https://openalex.org/W3035574324","https://openalex.org/W3118608800","https://openalex.org/W4237531625","https://openalex.org/W4285580171","https://openalex.org/W6610566761","https://openalex.org/W6624452987","https://openalex.org/W6629354409","https://openalex.org/W6631190155","https://openalex.org/W6640963894","https://openalex.org/W6674330103","https://openalex.org/W6688325169","https://openalex.org/W6717434760","https://openalex.org/W6718379498","https://openalex.org/W6732248266","https://openalex.org/W6732249622","https://openalex.org/W6734576159","https://openalex.org/W6737965738","https://openalex.org/W6738217336","https://openalex.org/W6741832134","https://openalex.org/W6743259963","https://openalex.org/W6743668032","https://openalex.org/W6744283655","https://openalex.org/W6755312952","https://openalex.org/W6756685634","https://openalex.org/W6765779288","https://openalex.org/W6779093361","https://openalex.org/W6780315314","https://openalex.org/W6787972765","https://openalex.org/W6840004463"],"related_works":["https://openalex.org/W2995777218","https://openalex.org/W3217069185","https://openalex.org/W4308928038","https://openalex.org/W3049340819","https://openalex.org/W4200430540","https://openalex.org/W3141413246","https://openalex.org/W2888032422","https://openalex.org/W4391792221","https://openalex.org/W4322709305","https://openalex.org/W2808862658"],"abstract_inverted_index":{"Generative":[0],"Adversarial":[1],"Networks":[2],"(GANs)":[3],"play":[4],"the":[5,12,15,20,30,36,50,54,68,72,82,110,124,138,158],"adversarial":[6,75],"game":[7],"between":[8],"two":[9],"neural":[10],"networks:":[11],"generator":[13,69],"and":[14,70,80,112,150,153],"discriminator.":[16,51],"Many":[17],"studies":[18],"treat":[19],"discriminator\u2019s":[21,37],"outputs":[22],"as":[23],"an":[24],"implicit":[25],"posterior":[26],"distribution":[27],"prior":[28],"to":[29,59,97,136],"input":[31],"image":[32],"distribution.":[33],"Thus,":[34],"increasing":[35,53],"output":[38,47,55,143],"dimensions":[39,56,144],"can":[40,65],"represent":[41],"richer":[42],"information":[43],"than":[44],"a":[45,60,92,104],"single":[46],"dimension":[48],"of":[49,74,85],"However,":[52],"will":[57],"lead":[58],"very":[61],"strong":[62],"discriminator,":[63],"which":[64],"easily":[66],"surpass":[67],"break":[71],"balance":[73],"learning.":[76],"Solving":[77],"such":[78],"conflict":[79,100],"elevating":[81],"generation":[83,139,161],"quality":[84,140],"GANs":[86],"remains":[87],"challenging.":[88],"Hence,":[89],"we":[90],"propose":[91],"simple":[93],"yet":[94],"effective":[95],"method":[96,107],"solve":[98],"this":[99],"problem":[101],"based":[102,122],"on":[103,123],"stochastic":[105],"selecting":[106],"by":[108],"extending":[109],"flipped":[111],"non-flipped":[113],"non-saturating":[114],"losses":[115],"in":[116,157],"BipGAN.":[117],"We":[118],"organized":[119],"our":[120,134],"experiments":[121,131],"famous":[125],"BigGAN":[126],"model":[127],"for":[128],"comparison.":[129],"Our":[130],"successfully":[132],"validated":[133],"approach":[135],"strengthening":[137],"within":[141],"limited":[142],"via":[145],"several":[146],"standard":[147],"evaluation":[148],"metrics":[149],"real-world":[151],"datasets":[152],"achieved":[154],"competitive":[155],"results":[156],"Human":[159],"face":[160],"task.":[162]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
