{"id":"https://openalex.org/W7156141030","doi":"https://doi.org/10.1145/3774904.3792255","title":"Expectation-Maximization Driven Contrastive Disentanglement for Generalized Category Discovery","display_name":"Expectation-Maximization Driven Contrastive Disentanglement for Generalized Category Discovery","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7156141030","doi":"https://doi.org/10.1145/3774904.3792255"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792255","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792255","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792255","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100755405","display_name":"Weiyi Yang","orcid":"https://orcid.org/0009-0009-9166-1739"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyi Yang","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-9166-1739","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027015677","display_name":"Richong Zhang","orcid":"https://orcid.org/0000-0002-1207-0300"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Richong Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1207-0300","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011910065","display_name":"J Y Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfan Chen","raw_affiliation_strings":["School of Software, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6807-0089","affiliations":[{"raw_affiliation_string":"School of Software, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080665569","display_name":"Jiawei Sheng","orcid":"https://orcid.org/0000-0002-4865-982X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Sheng","raw_affiliation_strings":["Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4865-982X","affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134698735","display_name":"Lihong Wang","orcid":"https://orcid.org/0000-0003-0179-2364"},"institutions":[{"id":"https://openalex.org/I4210087772","display_name":"National Computer Network Emergency Response Technical Team/Coordination Center of Chinar","ror":"https://ror.org/00247dh76","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210087772"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihong Wang","raw_affiliation_strings":["National Computer Network Emergency Response Technical Team / Coordination Center of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0179-2364","affiliations":[{"raw_affiliation_string":"National Computer Network Emergency Response Technical Team / Coordination Center of China, Beijing, China","institution_ids":["https://openalex.org/I4210087772"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7058","last_page":"7067"},"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":0.2766000032424927,"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":0.2766000032424927,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.14839999377727509,"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/T10028","display_name":"Topic Modeling","score":0.09549999982118607,"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/leverage","display_name":"Leverage (statistics)","score":0.6851000189781189},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5827000141143799},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.474700003862381},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4593999981880188},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.42980000376701355},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4025999903678894},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.3050000071525574}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6851000189781189},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.609499990940094},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5827000141143799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5555999875068665},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4535999894142151},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4447999894618988},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.42980000376701355},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26669999957084656},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2554999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2547999918460846},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792255","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792255","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792255","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792255","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6145153045654297}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2026653933","https://openalex.org/W2251410829","https://openalex.org/W2811300486","https://openalex.org/W2963341956","https://openalex.org/W2986193249","https://openalex.org/W2998721586","https://openalex.org/W3112240880","https://openalex.org/W3185674647","https://openalex.org/W4281719801","https://openalex.org/W4312281441","https://openalex.org/W4382202733","https://openalex.org/W4385568174","https://openalex.org/W4385570131","https://openalex.org/W4385573210","https://openalex.org/W4389520262","https://openalex.org/W4389520335","https://openalex.org/W4390873039","https://openalex.org/W4393147421","https://openalex.org/W4393160755","https://openalex.org/W4401043019","https://openalex.org/W4402670524","https://openalex.org/W4402683821","https://openalex.org/W4402770729"],"related_works":[],"abstract_inverted_index":{"Generalized":[0],"Category":[1],"Discovery":[2],"(GCD)":[3],"is":[4,190,199],"a":[5,123,141,155,160],"critical":[6],"task":[7],"in":[8,91],"open-world":[9],"computing":[10],"scenarios,":[11],"aiming":[12],"to":[13,39,58,76,84,107,134,144,164],"automatically":[14],"classify":[15],"partially":[16],"labeled":[17,49],"data":[18,50],"by":[19],"recognizing":[20],"both":[21],"known":[22,36,88,112],"and":[23,45,61,73,87,111,140,159,192],"novel":[24,52,65,86,110,120,137,169],"categories.":[25,53,66,113],"However,":[26],"existing":[27],"GCD":[28],"methods":[29],"usually":[30],"suffer":[31],"from":[32],"inherent":[33],"bias":[34,55],"toward":[35],"categories":[37,121,170],"due":[38],"the":[40,46,117],"exclusive":[41],"pre-training":[42],"on":[43,181],"them":[44],"absence":[47],"of":[48,51,119,168],"This":[54],"can":[56],"lead":[57],"significant":[59],"misclassification":[60],"clustering":[62],"errors":[63],"for":[64],"Although":[67],"recent":[68],"approaches":[69],"leverage":[70,150],"pseudo-label":[71],"training":[72],"contrastive":[74,152,157,162],"learning":[75],"address":[77,95],"this,":[78],"they":[79],"still":[80],"lack":[81],"explicit":[82],"supervision":[83],"disentangle":[85,109],"categories,":[89],"resulting":[90],"performance":[92],"bottlenecks.":[93],"To":[94],"these":[96],"limitations,":[97],"we":[98,149],"propose":[99],"an":[100,131],"Expectation-Maximization-driven":[101],"Contrastive":[102],"Disentanglement":[103],"(EMCD)":[104],"framework":[105],"designed":[106],"explicitly":[108],"We":[114],"particularly":[115],"formulate":[116],"identification":[118],"as":[122],"latent":[124],"variable":[125],"estimation":[126],"problem.":[127],"Specifically,":[128],"it":[129],"incorporates":[130],"EM-disentangling":[132],"regularization":[133,143],"softly":[135],"identify":[136],"category":[138],"samples":[139,167,175],"consistency":[142],"enhance":[145],"generalization.":[146],"In":[147],"addition,":[148],"dual":[151],"constraints,":[153],"including":[154],"cluster-sample":[156],"constraint":[158],"sample-sample":[161],"contrastive,":[163],"pull":[165],"close":[166],"while":[171],"pushing":[172],"apart":[173],"ambiguous":[174],"near":[176],"decision":[177],"boundaries.":[178],"Empirical":[179],"results":[180],"3":[182],"commonly":[183],"used":[184],"datasets":[185],"demonstrate":[186],"that":[187],"our":[188],"model":[189],"effective":[191],"outperforms":[193],"previous":[194],"state-of-the-art":[195],"methods.":[196],"Our":[197],"code":[198],"available":[200],"at":[201],"https://github.com/YWY-only/EMCD.":[202]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-28T00:00:00"}