{"id":"https://openalex.org/W3108989460","doi":"https://doi.org/10.1109/cvpr46437.2021.00918","title":"Fair Attribute Classification through Latent Space De-biasing","display_name":"Fair Attribute Classification through Latent Space De-biasing","publication_year":2021,"publication_date":"2021-06-01","ids":{"openalex":"https://openalex.org/W3108989460","doi":"https://doi.org/10.1109/cvpr46437.2021.00918","mag":"3108989460"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr46437.2021.00918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr46437.2021.00918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2012.01469","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073539430","display_name":"V. Ramaswamy","orcid":"https://orcid.org/0000-0002-0552-5338"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vikram V. Ramaswamy","raw_affiliation_strings":["Princeton University"],"affiliations":[{"raw_affiliation_string":"Princeton University","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061843649","display_name":"Sunnie S. Y. Kim","orcid":"https://orcid.org/0000-0002-8901-7233"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sunnie S. Y. Kim","raw_affiliation_strings":["Princeton University"],"affiliations":[{"raw_affiliation_string":"Princeton University","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022811687","display_name":"Olga Russakovsky","orcid":"https://orcid.org/0000-0001-5272-3241"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Olga Russakovsky","raw_affiliation_strings":["Princeton University"],"affiliations":[{"raw_affiliation_string":"Princeton University","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073539430"],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":1.41103575,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.83969962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"9297","last_page":"9306"},"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":0.9919999837875366,"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":0.9919999837875366,"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/T11448","display_name":"Face recognition and analysis","score":0.9800000190734863,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9697999954223633,"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/computer-science","display_name":"Computer science","score":0.721424400806427},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.6723867058753967},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.62762850522995},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5814321041107178},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5655918717384338},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4741196930408478},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43185630440711975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4201295077800751},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09661427140235901}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.721424400806427},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.6723867058753967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.62762850522995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5814321041107178},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5655918717384338},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4741196930408478},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43185630440711975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4201295077800751},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09661427140235901},{"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/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/cvpr46437.2021.00918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr46437.2021.00918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2012.01469","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.01469","pdf_url":"https://arxiv.org/pdf/2012.01469","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"","raw_type":null},{"id":"mag:3108989460","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2012.01469.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2012.01469","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2012.01469","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2012.01469","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.01469","pdf_url":"https://arxiv.org/pdf/2012.01469","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"","raw_type":null},"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3108989460.pdf","grobid_xml":"https://content.openalex.org/works/W3108989460.grobid-xml"},"referenced_works_count":71,"referenced_works":["https://openalex.org/W167016754","https://openalex.org/W1834627138","https://openalex.org/W2099471712","https://openalex.org/W2101234009","https://openalex.org/W2116666691","https://openalex.org/W2117539524","https://openalex.org/W2170612786","https://openalex.org/W2194775991","https://openalex.org/W2483215953","https://openalex.org/W2530395818","https://openalex.org/W2540757487","https://openalex.org/W2788481061","https://openalex.org/W2794583223","https://openalex.org/W2805173664","https://openalex.org/W2807869636","https://openalex.org/W2810873347","https://openalex.org/W2890680318","https://openalex.org/W2895471314","https://openalex.org/W2962760235","https://openalex.org/W2962770929","https://openalex.org/W2962787423","https://openalex.org/W2962879692","https://openalex.org/W2963078860","https://openalex.org/W2963116854","https://openalex.org/W2963290659","https://openalex.org/W2963349562","https://openalex.org/W2963373786","https://openalex.org/W2963767194","https://openalex.org/W2964121744","https://openalex.org/W2980688251","https://openalex.org/W2981479880","https://openalex.org/W2985060393","https://openalex.org/W2985068832","https://openalex.org/W2990751682","https://openalex.org/W2994873335","https://openalex.org/W3017048210","https://openalex.org/W3034431451","https://openalex.org/W3034523045","https://openalex.org/W3034638829","https://openalex.org/W3034700241","https://openalex.org/W3038239361","https://openalex.org/W3090695317","https://openalex.org/W3091560455","https://openalex.org/W3095857473","https://openalex.org/W3101298150","https://openalex.org/W3102061158","https://openalex.org/W3113409159","https://openalex.org/W3113580345","https://openalex.org/W3132103448","https://openalex.org/W4288083803","https://openalex.org/W4289258088","https://openalex.org/W6606837198","https://openalex.org/W6631190155","https://openalex.org/W6675354045","https://openalex.org/W6684642658","https://openalex.org/W6718379498","https://openalex.org/W6721933647","https://openalex.org/W6728551298","https://openalex.org/W6735913928","https://openalex.org/W6745560452","https://openalex.org/W6748382702","https://openalex.org/W6752461236","https://openalex.org/W6754610156","https://openalex.org/W6766182467","https://openalex.org/W6769911694","https://openalex.org/W6780051348","https://openalex.org/W6780226941","https://openalex.org/W6781390597","https://openalex.org/W6784001516","https://openalex.org/W6787380055","https://openalex.org/W6791024018"],"related_works":["https://openalex.org/W2788676105","https://openalex.org/W2990751682","https://openalex.org/W1852255964","https://openalex.org/W3102668440","https://openalex.org/W2754916687","https://openalex.org/W2782512342","https://openalex.org/W2798667726","https://openalex.org/W3157574109","https://openalex.org/W2966183206","https://openalex.org/W2292644546","https://openalex.org/W2940983946","https://openalex.org/W3162296486","https://openalex.org/W1857221572","https://openalex.org/W3041264590","https://openalex.org/W2979494843","https://openalex.org/W2886610278","https://openalex.org/W3086146559","https://openalex.org/W2969435189","https://openalex.org/W3175157109","https://openalex.org/W3159635074"],"abstract_inverted_index":{"Fairness":[0],"in":[1,20,28,79,137,151],"visual":[2],"recognition":[3,14],"is":[4,89],"becoming":[5],"a":[6,53,117,127],"prominent":[7],"and":[8,44,75,105,122,134,141,146],"critical":[9],"topic":[10],"of":[11,119],"discussion":[12],"as":[13],"systems":[15],"are":[16,32,40],"deployed":[17],"at":[18],"scale":[19],"the":[21,80,97,113,138,152],"real":[22],"world.":[23],"Models":[24],"trained":[25,111],"from":[26,65],"data":[27,87],"which":[29],"target":[30,58,109,132],"labels":[31,133],"correlated":[33],"with":[34,100],"protected":[35,93,135],"attributes":[36,136],"(e.g.,":[37],"gender,":[38],"race)":[39],"known":[41],"to":[42,71,84,148],"learn":[43],"exploit":[45],"those":[46],"correlations.":[47,67],"In":[48],"this":[49,101],"work,":[50],"we":[51],"introduce":[52],"method":[54],"for":[55,91],"training":[56,86],"accurate":[57],"classifiers":[59,110],"while":[60],"mitigating":[61],"biases":[62],"that":[63,88,108],"stem":[64],"these":[66,77],"We":[68,95,125],"use":[69],"GANs":[70],"generate":[72,85],"realistic-looking":[73],"images,":[74],"perturb":[76],"images":[78],"underlying":[81],"latent":[82],"space":[83],"balanced":[90],"each":[92],"attribute.":[94],"augment":[96],"original":[98],"dataset":[99,115],"perturbed":[102],"generated":[103],"data,":[104],"empirically":[106],"demonstrate":[107],"on":[112],"augmented":[114],"exhibit":[116],"number":[118],"both":[120],"quantitative":[121],"qualitative":[123],"benefits.":[124],"conduct":[126],"thorough":[128],"evaluation":[129],"across":[130],"multiple":[131],"CelebA":[139],"dataset,":[140],"provide":[142],"an":[143],"in-depth":[144],"analysis":[145],"comparison":[147],"existing":[149],"literature":[150],"space.":[153]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
