{"id":"https://openalex.org/W4385815462","doi":"https://doi.org/10.1109/cvprw59228.2023.00114","title":"The Universal Face Encoder: Learning Disentangled Representations Across Different Attributes","display_name":"The Universal Face Encoder: Learning Disentangled Representations Across Different Attributes","publication_year":2023,"publication_date":"2023-06-01","ids":{"openalex":"https://openalex.org/W4385815462","doi":"https://doi.org/10.1109/cvprw59228.2023.00114"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw59228.2023.00114","pdf_url":null,"source":{"id":"https://openalex.org/S4363607748","display_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","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/A5104090810","display_name":"Sandipan Banerjee","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]},{"id":"https://openalex.org/I4210133173","display_name":"Research!America (United States)","ror":"https://ror.org/044pgyv50","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133173"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sandipan Banerjee","raw_affiliation_strings":["Samsung Research America"],"affiliations":[{"raw_affiliation_string":"Samsung Research America","institution_ids":["https://openalex.org/I4210101778","https://openalex.org/I4210133173"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079005862","display_name":"Ajjen Joshi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113679","display_name":"Smart Eye (Sweden)","ror":"https://ror.org/01zcfag54","country_code":"SE","type":"company","lineage":["https://openalex.org/I4210113679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ajjen Joshi","raw_affiliation_strings":["Smart Eye"],"affiliations":[{"raw_affiliation_string":"Smart Eye","institution_ids":["https://openalex.org/I4210113679"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065191411","display_name":"Jay Turcot","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113679","display_name":"Smart Eye (Sweden)","ror":"https://ror.org/01zcfag54","country_code":"SE","type":"company","lineage":["https://openalex.org/I4210113679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jay Turcot","raw_affiliation_strings":["Smart Eye"],"affiliations":[{"raw_affiliation_string":"Smart Eye","institution_ids":["https://openalex.org/I4210113679"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.226,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.479469,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":65,"max":76},"biblio":{"volume":null,"issue":null,"first_page":"1071","last_page":"1080"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9999,"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"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9999,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9997,"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/T10057","display_name":"Face and Expression Recognition","score":0.9951,"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":0.6559589},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.51141185},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.50826234},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.49649888},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.46863627},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45828858}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7736317},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6559589},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.56528914},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5349373},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.51141185},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.50826234},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.49649888},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4949159},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.46863627},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.46528807},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45828858},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.44948962},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41444516},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.41033947},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.32194716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1268253},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw59228.2023.00114","pdf_url":null,"source":{"id":"https://openalex.org/S4363607748","display_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.78}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":45,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1565327149","https://openalex.org/W1731081199","https://openalex.org/W2096733369","https://openalex.org/W2194775991","https://openalex.org/W2331128040","https://openalex.org/W2345729520","https://openalex.org/W2560862806","https://openalex.org/W2603777577","https://openalex.org/W2806183416","https://openalex.org/W2912990735","https://openalex.org/W2934216562","https://openalex.org/W2940579548","https://openalex.org/W2951670304","https://openalex.org/W2953384591","https://openalex.org/W2962770929","https://openalex.org/W2962793481","https://openalex.org/W2962960082","https://openalex.org/W2963092440","https://openalex.org/W2963500702","https://openalex.org/W2963767194","https://openalex.org/W2963920537","https://openalex.org/W2981552720","https://openalex.org/W3005680577","https://openalex.org/W3034371424","https://openalex.org/W3034445277","https://openalex.org/W3034600949","https://openalex.org/W3035515747","https://openalex.org/W3035574324","https://openalex.org/W3043844802","https://openalex.org/W3104792420","https://openalex.org/W3107847401","https://openalex.org/W3108663077","https://openalex.org/W3112331073","https://openalex.org/W3119291591","https://openalex.org/W3138154797","https://openalex.org/W3173562028","https://openalex.org/W3194776748","https://openalex.org/W3202165894","https://openalex.org/W4214666522","https://openalex.org/W4214897085","https://openalex.org/W4287111106","https://openalex.org/W4288301576","https://openalex.org/W4312279169","https://openalex.org/W4321020667"],"related_works":["https://openalex.org/W834942123","https://openalex.org/W4232542516","https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W2103419012","https://openalex.org/W2095582735","https://openalex.org/W2068068201","https://openalex.org/W2059318893","https://openalex.org/W1989130879","https://openalex.org/W1965698851"],"abstract_inverted_index":{"Models":[0],"that":[1,36,79],"can":[2,37,82,139],"learn":[3],"orthogonal":[4],"representations":[5],"for":[6,20,91],"different":[7,40,115],"facial":[8,33,41],"attributes":[9,42,112,129],"(e.g.":[10],"pose,":[11],"lighting,":[12],"identity,":[13],"expressions)":[14],"have":[15],"proven":[16],"to":[17,60,72,86,124],"be":[18,84,140],"beneficial":[19],"both":[21],"discriminative":[22],"and":[23,57,67,126],"generative":[24],"tasks.":[25,95],"In":[26],"this":[27],"work,":[28],"we":[29],"propose":[30,52],"the":[31,65,98,120],"universal":[32],"encoder":[34],"(UFE)":[35],"simultaneously":[38],"encode":[39],"as":[43,142,148],"disentangled":[44],"features":[45,81],"from":[46,114,130],"a":[47,53,101],"single":[48],"face":[49,108],"image.":[50],"We":[51,76],"variety":[54],"of":[55,64,106,111,135],"qualitative":[56],"quantitative":[58],"metrics":[59],"evaluate":[61],"feature":[62],"orthogonality":[63],"UFE":[66,99,121],"demonstrate":[68],"superior":[69],"disentanglement":[70],"compared":[71],"traditional":[73],"single-attribute":[74],"encoding.":[75],"also":[77],"show":[78],"these":[80,128],"then":[83],"used":[85,141],"train":[87],"lightweight":[88],"prediction":[89],"heads":[90],"multiple":[92],"downstream":[93],"classification":[94],"Moreover,":[96],"coupling":[97],"with":[100],"style-based":[102],"decoder":[103],"enables":[104],"hallucination":[105],"new":[107],"images":[109],"composed":[110],"taken":[113],"samples.":[116],"As":[117],"experimentally":[118],"demonstrated,":[119],"allows":[122],"us":[123],"pick":[125],"choose":[127],"label-disjoint":[131],"datasets.":[132],"A":[133],"catalog":[134],"such":[136],"synthetic":[137],"composites":[138],"supplemental":[143],"training":[144],"data":[145],"or":[146],"simply":[147],"stock":[149],"photos.":[150]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4385815462","counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-04-11T07:21:59.855111","created_date":"2023-08-15"}