{"id":"https://openalex.org/W3168228541","doi":"https://doi.org/10.1109/cscwd49262.2021.9437750","title":"Using Bidirectional GAN with Improved Training Architecture for Imbalanced Tasks","display_name":"Using Bidirectional GAN with Improved Training Architecture for Imbalanced Tasks","publication_year":2021,"publication_date":"2021-05-05","ids":{"openalex":"https://openalex.org/W3168228541","doi":"https://doi.org/10.1109/cscwd49262.2021.9437750","mag":"3168228541"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd49262.2021.9437750","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd49262.2021.9437750","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100719744","display_name":"Wenjia Zhang","orcid":"https://orcid.org/0000-0002-9545-1124"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjia Zhang","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing,China","Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing,China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100416321","display_name":"Peng Peng","orcid":"https://orcid.org/0000-0002-3963-6482"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Peng","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing,China","Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing,China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046080277","display_name":"Heming Zhang","orcid":"https://orcid.org/0000-0002-3171-5805"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heming Zhang","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing,China","Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing,China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"714","last_page":"719"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9988999962806702,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9988999962806702,"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":0.9979000091552734,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9941999912261963,"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/computer-science","display_name":"Computer science","score":0.8114734292030334},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.7920584678649902},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6804212927818298},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5995718836784363},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5816758275032043},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5612375736236572},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5567189455032349},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5261349678039551},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46679314970970154},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45916399359703064},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4446328282356262},{"id":"https://openalex.org/keywords/premise","display_name":"Premise","score":0.43478164076805115},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.42188209295272827},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.42134395241737366},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07685866951942444}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8114734292030334},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.7920584678649902},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6804212927818298},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5995718836784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5816758275032043},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5612375736236572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5567189455032349},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5261349678039551},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46679314970970154},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45916399359703064},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4446328282356262},{"id":"https://openalex.org/C2778023277","wikidata":"https://www.wikidata.org/wiki/Q321703","display_name":"Premise","level":2,"score":0.43478164076805115},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42188209295272827},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.42134395241737366},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07685866951942444},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd49262.2021.9437750","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd49262.2021.9437750","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1685773568","display_name":null,"funder_award_id":"2020YFB1707803","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2032435122","https://openalex.org/W2103614420","https://openalex.org/W2104933073","https://openalex.org/W2119191234","https://openalex.org/W2133665775","https://openalex.org/W2148143831","https://openalex.org/W2779931100","https://openalex.org/W2786532017","https://openalex.org/W2787947370","https://openalex.org/W2806118840","https://openalex.org/W2962808998","https://openalex.org/W2963373786","https://openalex.org/W2970726449","https://openalex.org/W3007207078","https://openalex.org/W3026405303","https://openalex.org/W3089397955","https://openalex.org/W4294568686","https://openalex.org/W6675634716","https://openalex.org/W6747100823"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W4206637278","https://openalex.org/W4386005305","https://openalex.org/W3173198409","https://openalex.org/W3082051559","https://openalex.org/W1682621979","https://openalex.org/W3005996785","https://openalex.org/W4386984417","https://openalex.org/W2476099471","https://openalex.org/W3214668706"],"abstract_inverted_index":{"Classification":[0],"with":[1,44],"an":[2,49,57],"imbalanced":[3,128],"dataset":[4],"has":[5,114],"been":[6,115],"a":[7,11,119],"hard":[8],"task":[9],"for":[10,52],"long":[12],"time.":[13],"As":[14],"generative":[15],"models":[16],"become":[17],"more":[18,20],"and":[19,67,79,85],"attractive,":[21],"many":[22],"researchers":[23],"try":[24],"to":[25,29,99,105,117],"use":[26,62],"these":[27],"methods":[28],"address":[30],"the":[31,64,70,77,90,95],"imbalance":[32],"problem.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"employ":[38],"Bidirectional":[39],"Generative":[40],"Adversarial":[41],"Networks":[42],"(BIGAN)":[43],"improved":[45],"training":[46,71,110],"architecture":[47],"as":[48],"oversampling":[50],"method":[51,113,125],"minority":[53],"class.":[54],"We":[55,75],"introduce":[56],"additional":[58],"feedback":[59],"by":[60],"making":[61],"of":[63,72,92],"encoder":[65],"<tex>$E$</tex>":[66],"further":[68],"enhance":[69],"generator":[73],"<tex>$G$</tex>.":[74],"conduct":[76],"qualitative":[78],"quantitative":[80],"assessment":[81],"on":[82,126],"generated":[83,96],"samples,":[84],"it":[86,104],"shows":[87],"that":[88],"under":[89],"premise":[91],"ensuring":[93],"diversity,":[94],"data":[97,107],"tend":[98],"have":[100],"less":[101],"noise.":[102],"Using":[103],"perform":[106],"augmentation":[108],"in":[109],"process,":[111],"our":[112],"proved":[116],"get":[118],"better":[120],"performance":[121],"than":[122],"original":[123],"GAN-based":[124],"multi-class":[127],"classification":[129],"task.":[130]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}