{"id":"https://openalex.org/W4415108605","doi":"https://doi.org/10.1109/iccv51701.2025.01632","title":"Fair Generation without Unfair Distortions: Debiasing Text-To-Image Generation with Entanglement-Free Attention","display_name":"Fair Generation without Unfair Distortions: Debiasing Text-To-Image Generation with Entanglement-Free Attention","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4415108605","doi":"https://doi.org/10.1109/iccv51701.2025.01632"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.01632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.01632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.13298","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100688350","display_name":"Jeong-Hoon Park","orcid":"https://orcid.org/0009-0006-6728-8565"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]},{"id":"https://openalex.org/I4210099236","display_name":"Kootenay Association for Science & Technology","ror":"https://ror.org/011pv9p44","country_code":"CA","type":"nonprofit","lineage":["https://openalex.org/I4210099236"]}],"countries":["CA","KR"],"is_corresponding":false,"raw_author_name":"Jeonghoon Park","raw_affiliation_strings":["KAIST"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST","institution_ids":["https://openalex.org/I157485424","https://openalex.org/I4210099236"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324456","display_name":"Juyoung Lee","orcid":"https://orcid.org/0000-0001-5371-3707"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]},{"id":"https://openalex.org/I207623266","display_name":"Kao Corporation (Japan)","ror":"https://ror.org/016t1kc57","country_code":"JP","type":"company","lineage":["https://openalex.org/I207623266"]}],"countries":["JP","KR"],"is_corresponding":false,"raw_author_name":"Juyoung Lee","raw_affiliation_strings":["Kakao Corp","Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kakao Corp","institution_ids":["https://openalex.org/I207623266"]},{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030128483","display_name":"Chaeyeon Chung","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]},{"id":"https://openalex.org/I4210099236","display_name":"Kootenay Association for Science & Technology","ror":"https://ror.org/011pv9p44","country_code":"CA","type":"nonprofit","lineage":["https://openalex.org/I4210099236"]}],"countries":["CA","KR"],"is_corresponding":false,"raw_author_name":"Chaeyeon Chung","raw_affiliation_strings":["KAIST"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST","institution_ids":["https://openalex.org/I157485424","https://openalex.org/I4210099236"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021296521","display_name":"Jae\u2010Seong Lee","orcid":"https://orcid.org/0000-0003-0944-5172"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]},{"id":"https://openalex.org/I207623266","display_name":"Kao Corporation (Japan)","ror":"https://ror.org/016t1kc57","country_code":"JP","type":"company","lineage":["https://openalex.org/I207623266"]}],"countries":["JP","KR"],"is_corresponding":false,"raw_author_name":"Jaeseong Lee","raw_affiliation_strings":["Kakao Corp","Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kakao Corp","institution_ids":["https://openalex.org/I207623266"]},{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047912015","display_name":"Jaegul Choo","orcid":"https://orcid.org/0000-0003-1071-4835"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]},{"id":"https://openalex.org/I4210099236","display_name":"Kootenay Association for Science & Technology","ror":"https://ror.org/011pv9p44","country_code":"CA","type":"nonprofit","lineage":["https://openalex.org/I4210099236"]}],"countries":["CA","KR"],"is_corresponding":false,"raw_author_name":"Jaegul Choo","raw_affiliation_strings":["KAIST"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST","institution_ids":["https://openalex.org/I157485424","https://openalex.org/I4210099236"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055994909","display_name":"Jindong Gu","orcid":"https://orcid.org/0009-0000-0574-0129"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jindong Gu","raw_affiliation_strings":["University of Oxford"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40186916,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"17567","last_page":"17576"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13877","display_name":"Law in Society and Culture","score":0.7342000007629395,"subfield":{"id":"https://openalex.org/subfields/3308","display_name":"Law"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13877","display_name":"Law in Society and Culture","score":0.7342000007629395,"subfield":{"id":"https://openalex.org/subfields/3308","display_name":"Law"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13851","display_name":"Law, AI, and Intellectual Property","score":0.6606000065803528,"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/debiasing","display_name":"Debiasing","score":0.9429000020027161},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5293999910354614},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4300000071525574},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.3625999987125397},{"id":"https://openalex.org/keywords/unintended-consequences","display_name":"Unintended consequences","score":0.35440000891685486},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.3337000012397766},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.3111000061035156},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.305400013923645}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9429000020027161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6151999831199646},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5293999910354614},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4300000071525574},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3601999878883362},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3587000072002411},{"id":"https://openalex.org/C2776889888","wikidata":"https://www.wikidata.org/wiki/Q1135789","display_name":"Unintended consequences","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C189216375","wikidata":"https://www.wikidata.org/wiki/Q1127759","display_name":"Cognitive bias","level":3,"score":0.29809999465942383},{"id":"https://openalex.org/C79585631","wikidata":"https://www.wikidata.org/wiki/Q431498","display_name":"Confirmation bias","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2969000041484833},{"id":"https://openalex.org/C147077947","wikidata":"https://www.wikidata.org/wiki/Q1515895","display_name":"Socioeconomic status","level":3,"score":0.28940001130104065},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C45555294","wikidata":"https://www.wikidata.org/wiki/Q28113351","display_name":"Inequality","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C159447121","wikidata":"https://www.wikidata.org/wiki/Q490535","display_name":"Response bias","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C136566586","wikidata":"https://www.wikidata.org/wiki/Q29598","display_name":"Status quo bias","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.258899986743927},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2502000033855438}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.01632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.01632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2506.13298","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.13298","pdf_url":"https://arxiv.org/pdf/2506.13298","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2506.13298","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.13298","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2506.13298","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.13298","pdf_url":"https://arxiv.org/pdf/2506.13298","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,40,126,149],"diffusion-based":[3],"text-to-image":[4],"(T2I)":[5],"models":[6],"have":[7],"enabled":[8],"the":[9,61,71,124,131,158],"generation":[10],"of":[11,138],"high-quality":[12],"and":[13,28,36,99,122,163],"photorealistic":[14],"images":[15],"from":[16],"text.":[17],"However,":[18],"they":[19,50],"often":[20,51],"exhibit":[21],"societal":[22],"biases":[23],"related":[24],"to":[25,57,60,129],"gender,":[26],"race,":[27],"socioeconomic":[29],"status,":[30],"thereby":[31,156],"potentially":[32],"reinforcing":[33],"harmful":[34],"stereotypes":[35],"shaping":[37],"public":[38],"perception":[39],"unintended":[41],"ways.":[42],"While":[43],"existing":[44,147],"bias":[45,62,72,108,151],"mitigation":[46],"methods":[47,148],"demonstrate":[48,143],"effectiveness,":[49],"encounter":[52],"attribute":[53,118],"entanglement,":[54],"where":[55],"adjustments":[56],"attributes":[58,68,95,104],"relevant":[59],"(i.e.,":[63,73],"target":[64,94,117,139],"attributes)":[65],"unintentionally":[66],"alter":[67],"unassociated":[69],"with":[70,119],"non-target":[74,103,154],"attributes),":[75],"causing":[76],"undesirable":[77],"distribution":[78,137,162],"shifts.":[79],"To":[80],"address":[81],"this":[82],"challenge,":[83],"we":[84],"introduce":[85],"Entanglement-Free":[86],"Attention":[87],"(EFA),":[88],"a":[89,116,135],"method":[90],"that":[91,144],"accurately":[92],"incorporates":[93],"(e.g.,":[96,105],"White,":[97],"Black,":[98],"Asian)":[100],"while":[101,152],"preserving":[102,153],"background)":[106],"during":[107],"mitigation.":[109],"At":[110],"inference":[111],"time,":[112],"EFA":[113,145],"randomly":[114],"samples":[115],"equal":[120],"probability":[121],"adjusts":[123],"cross-attention":[125],"selected":[127],"layers":[128],"incorporate":[130],"sampled":[132],"attribute,":[133],"achieving":[134],"fair":[136],"attributes.":[140],"Extensive":[141],"experiments":[142],"outperforms":[146],"mitigating":[150],"attributes,":[155],"maintaining":[157],"original":[159],"model's":[160],"output":[161],"generative":[164],"capacity.":[165]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-13T00:00:00"}
