{"id":"https://openalex.org/W3035510274","doi":"https://doi.org/10.24963/ijcai.2020/130","title":"Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation","display_name":"Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3035510274","doi":"https://doi.org/10.24963/ijcai.2020/130","mag":"3035510274"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/130","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/130","pdf_url":"https://www.ijcai.org/proceedings/2020/0130.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0130.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113928170","display_name":"Jiang Pin","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pin Jiang","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China","Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066818276","display_name":"Aming Wu","orcid":"https://orcid.org/0000-0001-8426-0392"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aming Wu","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China","Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031819155","display_name":"Yahong Han","orcid":"https://orcid.org/0000-0003-2768-1398"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yahong Han","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China","Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015563699","display_name":"Yunfeng Shao","orcid":"https://orcid.org/0000-0002-4335-5157"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Yunfeng Shao","raw_affiliation_strings":["Huawei Noah's Ark Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040309197","display_name":"Meiyu Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Meiyu Qi","raw_affiliation_strings":["Huawei Noah's Ark Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102635565","display_name":"Bingshuai Li","orcid":"https://orcid.org/0000-0001-7655-6919"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Bingshuai Li","raw_affiliation_strings":["Huawei Noah's Ark Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab","institution_ids":["https://openalex.org/I4210159102"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":97,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"934","last_page":"940"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9966999888420105,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9966999888420105,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.991100013256073,"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/T11242","display_name":"Nuclear Materials and Properties","score":0.9415000081062317,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.9422979354858398},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7959862947463989},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.663598895072937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6595946550369263},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6347962617874146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5718478560447693},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.529026985168457},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.47829943895339966},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.45443859696388245},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11669608950614929},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.09000271558761597}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9422979354858398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7959862947463989},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.663598895072937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6595946550369263},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6347962617874146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5718478560447693},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.529026985168457},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.47829943895339966},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.45443859696388245},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11669608950614929},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.09000271558761597},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/130","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/130","pdf_url":"https://www.ijcai.org/proceedings/2020/0130.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/130","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/130","pdf_url":"https://www.ijcai.org/proceedings/2020/0130.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4000000059604645,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1829462488","display_name":"\u8de8\u5a92\u4f53\u667a\u80fd\u95ee\u7b54\u548c\u63a8\u7406\u5173\u952e\u7406\u8bba\u4e0e\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61932009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4407339161","display_name":null,"funder_award_id":"61876130","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G947164805","display_name":null,"funder_award_id":"61876130, 61932009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3035510274.pdf","grobid_xml":"https://content.openalex.org/works/W3035510274.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1882958252","https://openalex.org/W1945616565","https://openalex.org/W2159291411","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2293363371","https://openalex.org/W2592691248","https://openalex.org/W2795155917","https://openalex.org/W2945328857","https://openalex.org/W2951970475","https://openalex.org/W2952229419","https://openalex.org/W2953070460","https://openalex.org/W2962687275","https://openalex.org/W2963080758","https://openalex.org/W2963207607","https://openalex.org/W2963826681","https://openalex.org/W2963857521","https://openalex.org/W2964139811","https://openalex.org/W2964153729","https://openalex.org/W2964159205","https://openalex.org/W2964278684","https://openalex.org/W2978426779","https://openalex.org/W2981720610","https://openalex.org/W2986381065","https://openalex.org/W4300996741"],"related_works":["https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W3203792196","https://openalex.org/W2955455867","https://openalex.org/W4295929828","https://openalex.org/W3156096827","https://openalex.org/W4287210399"],"abstract_inverted_index":{"Semi-supervised":[0],"domain":[1,21,36,59,100,123,135],"adaptation":[2],"(SSDA)":[3],"is":[4,41,85],"a":[5,68,108,150],"novel":[6],"branch":[7],"of":[8,27,166],"machine":[9],"learning":[10],"that":[11],"scarce":[12],"labeled":[13],"target":[14,99,134,143],"examples":[15,120],"are":[16,47],"available,":[17],"compared":[18],"with":[19,49],"unsupervised":[20],"adaptation.":[22],"To":[23],"make":[24],"effective":[25],"use":[26],"these":[28],"additional":[29,50],"data":[30],"so":[31],"as":[32],"to":[33,42,66,87,98,117,133,144,156],"bridge":[34],"the":[35,53,58,75,96,122,126,164,176,180],"gap,":[37,124],"one":[38],"possible":[39],"way":[40],"generate":[43,92],"adversarial":[44,77,111,119,159],"examples,":[45],"which":[46,84],"images":[48],"perturbations,":[51],"between":[52,89],"two":[54],"domains":[55],"and":[56,101,114,136,172],"fill":[57],"gap.":[60],"Adversarial":[61,128,139,152],"training":[62,78,112],"has":[63],"been":[64],"proven":[65],"be":[67],"powerful":[69],"method":[70,113,178],"for":[71,131,142],"this":[72,104],"purpose.":[73],"However,":[74],"traditional":[76],"adds":[79],"noises":[80,94],"in":[81],"arbitrary":[82],"directions,":[83],"inefficient":[86],"migrate":[88],"domains,":[90],"or":[91],"directional":[93],"from":[95],"source":[97,132,145],"reverse.":[102],"In":[103],"work,":[105],"we":[106,148],"devise":[107,149],"general":[109],"bidirectional":[110],"employ":[115],"gradient":[116],"guide":[118],"across":[121],"i.e.,":[125],"Adaptive":[127],"Training":[129,140,153],"(AAT)":[130],"Entropy-penalized":[137],"Virtual":[138],"(E-VAT)":[141],"domain.":[146],"Particularly,":[147],"Bidirectional":[151],"(BiAT)":[154],"network":[155],"perform":[157],"diverse":[158],"trainings":[160],"jointly.":[161],"We":[162],"evaluate":[163],"effectiveness":[165],"BiAT":[167],"on":[168],"three":[169],"benchmark":[170],"datasets":[171],"experimental":[173],"results":[174],"demonstrate":[175],"proposed":[177],"achieves":[179],"state-of-the-art.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":30},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":21},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
