{"id":"https://openalex.org/W4312246223","doi":"https://doi.org/10.1109/icpr56361.2022.9956437","title":"Discriminative Mutual Learning for Multi-target Domain Adaptation","display_name":"Discriminative Mutual Learning for Multi-target Domain Adaptation","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312246223","doi":"https://doi.org/10.1109/icpr56361.2022.9956437"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956437","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956437","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5072667046","display_name":"Jie Wang","orcid":"https://orcid.org/0000-0001-5500-2060"},"institutions":[{"id":"https://openalex.org/I4210094759","display_name":"Fujitsu (United States)","ror":"https://ror.org/0073whr05","country_code":"US","type":"company","lineage":["https://openalex.org/I2252096349","https://openalex.org/I4210094759"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Wang","raw_affiliation_strings":["Fujitsu R&D Center, Co., Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu R&D Center, Co., Ltd","institution_ids":["https://openalex.org/I4210094759"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065997113","display_name":"Chaoliang Zhong","orcid":"https://orcid.org/0000-0003-1697-6932"},"institutions":[{"id":"https://openalex.org/I4210094759","display_name":"Fujitsu (United States)","ror":"https://ror.org/0073whr05","country_code":"US","type":"company","lineage":["https://openalex.org/I2252096349","https://openalex.org/I4210094759"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaoliang Zhong","raw_affiliation_strings":["Fujitsu R&D Center, Co., Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu R&D Center, Co., Ltd","institution_ids":["https://openalex.org/I4210094759"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101832747","display_name":"Cheng Feng","orcid":"https://orcid.org/0000-0003-3861-0042"},"institutions":[{"id":"https://openalex.org/I4210094759","display_name":"Fujitsu (United States)","ror":"https://ror.org/0073whr05","country_code":"US","type":"company","lineage":["https://openalex.org/I2252096349","https://openalex.org/I4210094759"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cheng Feng","raw_affiliation_strings":["Fujitsu R&D Center, Co., Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu R&D Center, Co., Ltd","institution_ids":["https://openalex.org/I4210094759"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386104","display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0002-2674-1638"},"institutions":[{"id":"https://openalex.org/I4210094759","display_name":"Fujitsu (United States)","ror":"https://ror.org/0073whr05","country_code":"US","type":"company","lineage":["https://openalex.org/I2252096349","https://openalex.org/I4210094759"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Zhang","raw_affiliation_strings":["Fujitsu R&D Center, Co., Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu R&D Center, Co., Ltd","institution_ids":["https://openalex.org/I4210094759"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327195","display_name":"Jun Sun","orcid":"https://orcid.org/0000-0002-0967-4859"},"institutions":[{"id":"https://openalex.org/I4210094759","display_name":"Fujitsu (United States)","ror":"https://ror.org/0073whr05","country_code":"US","type":"company","lineage":["https://openalex.org/I2252096349","https://openalex.org/I4210094759"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Sun","raw_affiliation_strings":["Fujitsu R&D Center, Co., Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu R&D Center, Co., Ltd","institution_ids":["https://openalex.org/I4210094759"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064928162","display_name":"Yasuto Yokota","orcid":"https://orcid.org/0000-0003-0512-6318"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yasuto Yokota","raw_affiliation_strings":["Fujitsu Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Ltd","institution_ids":["https://openalex.org/I4210159607"]}]}],"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":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2900","last_page":"2906"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994000196456909,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994000196456909,"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/T10515","display_name":"Cancer-related molecular mechanisms research","score":0.9355000257492065,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.914900004863739,"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/discriminative-model","display_name":"Discriminative model","score":0.9360302686691284},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.7724200487136841},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7612541913986206},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6927310824394226},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6783267259597778},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6500759720802307},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5834738612174988},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5245152711868286},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5030891299247742},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.4974253475666046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4721118211746216},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12437784671783447},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.052771031856536865}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9360302686691284},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7724200487136841},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7612541913986206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6927310824394226},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6783267259597778},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6500759720802307},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5834738612174988},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5245152711868286},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5030891299247742},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.4974253475666046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4721118211746216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12437784671783447},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.052771031856536865},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956437","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956437","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1722318740","https://openalex.org/W1731081199","https://openalex.org/W1836465849","https://openalex.org/W1904365287","https://openalex.org/W2031342017","https://openalex.org/W2102605133","https://openalex.org/W2115403315","https://openalex.org/W2117539524","https://openalex.org/W2156387975","https://openalex.org/W2159291411","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2279034837","https://openalex.org/W2293363371","https://openalex.org/W2296073425","https://openalex.org/W2478454054","https://openalex.org/W2520774990","https://openalex.org/W2593768305","https://openalex.org/W2627183927","https://openalex.org/W2788768841","https://openalex.org/W2795155917","https://openalex.org/W2803297029","https://openalex.org/W2895281799","https://openalex.org/W2899771611","https://openalex.org/W2940579548","https://openalex.org/W2962687275","https://openalex.org/W2963037989","https://openalex.org/W2963240485","https://openalex.org/W2963449430","https://openalex.org/W2963735582","https://openalex.org/W2963858297","https://openalex.org/W2964278684","https://openalex.org/W2964288524","https://openalex.org/W2981720610","https://openalex.org/W2982204955","https://openalex.org/W2998043922","https://openalex.org/W3003384866","https://openalex.org/W3034478348","https://openalex.org/W3043064029","https://openalex.org/W3088032605","https://openalex.org/W3118945974","https://openalex.org/W3134748702","https://openalex.org/W3161937211","https://openalex.org/W3162278040","https://openalex.org/W3183605589","https://openalex.org/W6628973269","https://openalex.org/W6637542466","https://openalex.org/W6637618735","https://openalex.org/W6638667902","https://openalex.org/W6640036494","https://openalex.org/W6681588610","https://openalex.org/W6682889407","https://openalex.org/W6683633756","https://openalex.org/W6695692224","https://openalex.org/W6713955831","https://openalex.org/W6720111944","https://openalex.org/W6726946684","https://openalex.org/W6745986955","https://openalex.org/W6750109254","https://openalex.org/W6751635369","https://openalex.org/W6751647823","https://openalex.org/W6755287564","https://openalex.org/W6756040250","https://openalex.org/W6761687776","https://openalex.org/W6766414560","https://openalex.org/W6783445175","https://openalex.org/W6791252336"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W4394775207","https://openalex.org/W3095487414","https://openalex.org/W2901026139"],"abstract_inverted_index":{"Unsupervised":[0],"domain":[1,19,31,68,74,80,137],"adaptation":[2,32,69,75,81,138],"(UDA)":[3],"has":[4],"attracted":[5],"much":[6],"attention":[7],"among":[8],"those":[9],"seeking":[10],"to":[11,20,46,52,94,105,132],"transfer":[12],"a":[13,16,53,60,123],"model":[14,108],"from":[15],"labeled":[17],"source":[18],"an":[21],"unlabeled":[22],"target":[23,30,48,55,73,79,111],"domain.":[24,56],"Many":[25],"effective":[26],"algorithms":[27],"for":[28,66,98],"single":[29],"(STDA)":[33],"have":[34],"been":[35],"designed,":[36],"however,":[37],"STDA":[38],"cannot":[39],"satisfy":[40],"the":[41,89,102,107,140,144],"scenarios":[42],"of":[43],"transferring":[44,51],"simultaneously":[45],"multiple":[47,78],"domains":[49],"or":[50],"blending":[54,72],"This":[57],"paper":[58],"proposes":[59],"novel":[61],"discriminative":[62,96],"mutual":[63,124],"learning":[64,125],"method":[65,142],"multi-target":[67],"covering":[70],"both":[71,148],"(BTDA)":[76],"and":[77,101,150],"(MTDA).":[82],"Two":[83],"key":[84],"points":[85],"are":[86,120],"considered":[87],"in":[88,147],"proposed":[90,141],"method:":[91],"one":[92],"is":[93,104],"learn":[95],"features":[97],"better":[99],"prediction,":[100],"other":[103],"self-train":[106],"with":[109],"pseudo-labeled":[110],"data":[112],"based":[113],"on":[114,135],"distance":[115],"information.":[116],"These":[117],"two":[118,128],"aspects":[119],"integrated":[121],"through":[122],"strategy":[126],"via":[127],"different":[129],"classifiers.":[130],"According":[131],"extensive":[133],"experiments":[134],"three":[136],"benchmarks,":[139],"demonstrates":[143],"state-of-the-art":[145],"performance":[146],"BTDA":[149],"MTDA":[151],"settings.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}