{"id":"https://openalex.org/W2999289173","doi":"https://doi.org/10.1109/access.2020.2964315","title":"An Incremental Self-Labeling Strategy for Semi-Supervised Deep Learning Based on Generative Adversarial Networks","display_name":"An Incremental Self-Labeling Strategy for Semi-Supervised Deep Learning Based on Generative Adversarial Networks","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W2999289173","doi":"https://doi.org/10.1109/access.2020.2964315","mag":"2999289173"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2964315","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2964315","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08950402.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/ielx7/6287639/8948470/08950402.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103082974","display_name":"Xiaotao Wei","orcid":"https://orcid.org/0000-0002-9085-9446"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaotao Wei","raw_affiliation_strings":["School of Software Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9085-9446","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053261798","display_name":"Xiang Wei","orcid":"https://orcid.org/0000-0002-8967-6423"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Wei","raw_affiliation_strings":["School of Software Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8967-6423","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007723033","display_name":"Weiwei Xing","orcid":"https://orcid.org/0000-0002-6378-926X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Xing","raw_affiliation_strings":["School of Software Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6378-926X","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102880475","display_name":"Siyang Lu","orcid":"https://orcid.org/0000-0003-2952-4441"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyang Lu","raw_affiliation_strings":["Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2952-4441","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100627858","display_name":"Wei Lu","orcid":"https://orcid.org/0000-0002-4574-3209"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Lu","raw_affiliation_strings":["School of Software Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4574-3209","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.3038,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.84313516,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"8","issue":null,"first_page":"8913","last_page":"8921"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9987999796867371,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980000257492065,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8766646385192871},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8672770261764526},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6798677444458008},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6640934944152832},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5858063697814941},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5814527869224548},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5001041889190674},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4665510356426239},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44442418217658997},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.44096267223358154},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43296247720718384},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.41326093673706055},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3359607756137848}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8766646385192871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8672770261764526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6798677444458008},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6640934944152832},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5858063697814941},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5814527869224548},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5001041889190674},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4665510356426239},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44442418217658997},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.44096267223358154},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43296247720718384},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.41326093673706055},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3359607756137848},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2964315","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2964315","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08950402.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:25621d438e89430d8a003fd483e0521e","is_oa":true,"landing_page_url":"https://doaj.org/article/25621d438e89430d8a003fd483e0521e","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 8, Pp 8913-8921 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2964315","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2964315","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08950402.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":[],"awards":[{"id":"https://openalex.org/G1802926189","display_name":null,"funder_award_id":"2019RC025","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2999289173.pdf","grobid_xml":"https://content.openalex.org/works/W2999289173.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W830076066","https://openalex.org/W1985133440","https://openalex.org/W2099471712","https://openalex.org/W2112796928","https://openalex.org/W2335728318","https://openalex.org/W2412510955","https://openalex.org/W2530816535","https://openalex.org/W2592691248","https://openalex.org/W2596763562","https://openalex.org/W2765452429","https://openalex.org/W2768252793","https://openalex.org/W2908698261","https://openalex.org/W2909869271","https://openalex.org/W2910712023","https://openalex.org/W2919115771","https://openalex.org/W2943889198","https://openalex.org/W2949985837","https://openalex.org/W2951970475","https://openalex.org/W2952229419","https://openalex.org/W2953070460","https://openalex.org/W2962808998","https://openalex.org/W2963080758","https://openalex.org/W2963250052","https://openalex.org/W2963373786","https://openalex.org/W2963558289","https://openalex.org/W2963584589","https://openalex.org/W2963697299","https://openalex.org/W2964057425","https://openalex.org/W2964159205","https://openalex.org/W2964218010","https://openalex.org/W2964292098","https://openalex.org/W2964317695","https://openalex.org/W2973077827","https://openalex.org/W2977961330","https://openalex.org/W2979744195","https://openalex.org/W2979805229","https://openalex.org/W2982376094","https://openalex.org/W2993467542","https://openalex.org/W3118608800","https://openalex.org/W3209458476","https://openalex.org/W4320013936","https://openalex.org/W6623329352","https://openalex.org/W6685777725","https://openalex.org/W6703116779","https://openalex.org/W6714590955","https://openalex.org/W6715189028","https://openalex.org/W6718379498","https://openalex.org/W6733814495","https://openalex.org/W6736098614","https://openalex.org/W6740898809","https://openalex.org/W6745694482","https://openalex.org/W6749097180","https://openalex.org/W6757712183","https://openalex.org/W6758069156","https://openalex.org/W6764051988","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W4406754633","https://openalex.org/W3138936091","https://openalex.org/W2516165723","https://openalex.org/W4287241967","https://openalex.org/W3144173820","https://openalex.org/W4298370744","https://openalex.org/W2555363131"],"abstract_inverted_index":{"The":[0,140],"recent":[1],"success":[2],"of":[3,21,28,38,123,174,186],"deep":[4],"neural":[5],"networks":[6],"is":[7],"attributed":[8],"in":[9,43],"part":[10],"to":[11,97,103,119,150],"large-scale":[12],"well-labeled":[13],"training":[14,76,130],"data.":[15],"However,":[16],"with":[17,25,70,125,167,180,196,204],"the":[18,26,39,75,80,106,121,129,135,145,156,193],"ever-increasing":[19],"size":[20],"modern":[22],"datasets,":[23],"combined":[24],"difficulty":[27],"obtaining":[29],"label":[30,113],"information,":[31],"semi-supervised":[32,152],"learning":[33,153],"(SSL)":[34],"has":[35],"become":[36],"one":[37],"most":[40],"remarkable":[41],"issues":[42],"data":[44,69,94,104],"analysis.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49,84,108,132],"propose":[50],"an":[51],"Incremental":[52],"Self-Labeling":[53],"strategy":[54,89],"for":[55,73,90,155],"SSL":[56],"based":[57],"on":[58],"Generative":[59],"Adversarial":[60],"Nets":[61],"(ISL-GAN),":[62],"which":[63],"functions":[64],"by":[65],"constantly":[66],"assigning":[67],"unlabeled":[68],"virtual":[71,81,101],"labels":[72,102],"promoting":[74],"process.":[77],"Specifically,":[78],"during":[79,105,128],"labeling":[82],"process,":[83,131],"introduce":[85,134],"a":[86,110,172,183,197],"temporal-based":[87],"self-labeling":[88],"safe":[91],"and":[92,115,159,191,207],"stable":[93],"labeling.":[95],"Then,":[96],"dynamically":[98],"assign":[99],"more":[100],"training,":[107],"conduct":[109],"phased":[111],"incremental":[112],"screening":[114],"updating":[116],"strategy.":[117],"Finally,":[118],"balance":[120],"contribution":[122],"samples":[124],"different":[126],"loss":[127],"further":[133],"Balance":[136],"factor":[137],"Term":[138],"(BT).":[139],"experimental":[141],"results":[142,154],"show":[143],"that":[144],"proposed":[146],"method":[147],"gives":[148],"rise":[149],"state-of-the-art":[151],"MNIST,":[157],"CIFAR-10,":[158],"SVHN":[160,210],"datasets.":[161,211],"Particularly,":[162],"our":[163],"model":[164],"performs":[165],"well":[166],"fewer":[168],"labeled":[169,177],"conditions.":[170],"With":[171],"dataset":[173],"only":[175],"1,000":[176,208],"CIFAR-10":[178],"images":[179],"CONV-Large":[181],"Net,":[182],"test":[184,199],"error":[185,200],"11.2%":[187],"can":[188,201],"be":[189,202],"achieved,":[190],"nearly":[192],"same":[194],"performance":[195],"3.5%":[198],"achieved":[203],"both":[205],"500":[206],"image-labeled":[209]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
