{"id":"https://openalex.org/W3042545748","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534045","title":"Unsupervised Controllable Generation with Self-Training","display_name":"Unsupervised Controllable Generation with Self-Training","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3042545748","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534045","mag":"3042545748"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/2007.09250","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004562735","display_name":"Grigorios G. Chrysos","orcid":"https://orcid.org/0000-0002-0650-1856"},"institutions":[{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Grigorios G Chrysos","raw_affiliation_strings":["EPFL, Switzerland","\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne,"],"affiliations":[{"raw_affiliation_string":"EPFL, Switzerland","institution_ids":["https://openalex.org/I5124864"]},{"raw_affiliation_string":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne,","institution_ids":["https://openalex.org/I5124864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006048318","display_name":"Jean Kossaifi","orcid":"https://orcid.org/0000-0002-4445-3429"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jean Kossaifi","raw_affiliation_strings":["NVIDIA, United States"],"affiliations":[{"raw_affiliation_string":"NVIDIA, United States","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054586129","display_name":"Zhiding Yu","orcid":"https://orcid.org/0000-0003-1776-996X"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]},{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Zhiding Yu","raw_affiliation_strings":["NVIDIA, United States","nVidia"],"affiliations":[{"raw_affiliation_string":"NVIDIA, United States","institution_ids":["https://openalex.org/I4210127875"]},{"raw_affiliation_string":"nVidia","institution_ids":["https://openalex.org/I1304085615"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014498545","display_name":"Anima Anandkumar","orcid":"https://orcid.org/0000-0002-6974-6797"},"institutions":[{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]},{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Anima Anandkumar","raw_affiliation_strings":["Caltech / NVIDIA, United States","nVidia"],"affiliations":[{"raw_affiliation_string":"Caltech / NVIDIA, United States","institution_ids":["https://openalex.org/I4210127875"]},{"raw_affiliation_string":"nVidia","institution_ids":["https://openalex.org/I1304085615"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004562735"],"corresponding_institution_ids":["https://openalex.org/I5124864"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00428203,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9995999932289124,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9995999932289124,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9783999919891357,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9761000275611877,"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/discriminator","display_name":"Discriminator","score":0.8640637397766113},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7420095205307007},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.6704186201095581},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6524027585983276},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.6170282363891602},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5368595123291016},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5233646631240845},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.49464914202690125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47650495171546936},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.46546244621276855},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4253077805042267},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.41785481572151184},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2795839309692383},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.2458217442035675}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8640637397766113},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7420095205307007},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6704186201095581},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6524027585983276},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.6170282363891602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5368595123291016},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5233646631240845},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.49464914202690125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47650495171546936},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.46546244621276855},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4253077805042267},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.41785481572151184},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2795839309692383},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2458217442035675},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","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},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2007.09250","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.09250","pdf_url":"https://arxiv.org/pdf/2007.09250","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":"text"},{"id":"pmh:oai:authors.library.caltech.edu:106485","is_oa":true,"landing_page_url":"https://authors.library.caltech.edu/106485/","pdf_url":"https://resolver.caltech.edu/CaltechAUTHORS:20201106-120158552","source":{"id":"https://openalex.org/S4306402161","display_name":"CaltechAUTHORS (California Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I122411786","host_organization_name":"California Institute of Technology","host_organization_lineage":["https://openalex.org/I122411786"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Book Section"},{"id":"mag:3042545748","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2007.09250.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":"pmh:oai:infoscience.epfl.ch:291302","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/291302","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference proceedings"},{"id":"doi:10.48550/arxiv.2007.09250","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2007.09250","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:2007.09250","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.09250","pdf_url":"https://arxiv.org/pdf/2007.09250","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":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.75,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":79,"referenced_works":["https://openalex.org/W1583912456","https://openalex.org/W1834627138","https://openalex.org/W2099741732","https://openalex.org/W2105724942","https://openalex.org/W2157617585","https://openalex.org/W2416462230","https://openalex.org/W2544822491","https://openalex.org/W2596763562","https://openalex.org/W2737047298","https://openalex.org/W2750823225","https://openalex.org/W2753738274","https://openalex.org/W2767614780","https://openalex.org/W2785519580","https://openalex.org/W2890140546","https://openalex.org/W2898269661","https://openalex.org/W2899771611","https://openalex.org/W2903141607","https://openalex.org/W2903538854","https://openalex.org/W2904922784","https://openalex.org/W2908006003","https://openalex.org/W2920223497","https://openalex.org/W2953327099","https://openalex.org/W2962695743","https://openalex.org/W2962738009","https://openalex.org/W2962764349","https://openalex.org/W2962770929","https://openalex.org/W2962785568","https://openalex.org/W2962907415","https://openalex.org/W2962942997","https://openalex.org/W2963008857","https://openalex.org/W2963082441","https://openalex.org/W2963836885","https://openalex.org/W2963981733","https://openalex.org/W2964127395","https://openalex.org/W2967013449","https://openalex.org/W2967826146","https://openalex.org/W2970371739","https://openalex.org/W2970754141","https://openalex.org/W2996582215","https://openalex.org/W2997473052","https://openalex.org/W2999811719","https://openalex.org/W3004719090","https://openalex.org/W3010729701","https://openalex.org/W3024325475","https://openalex.org/W3035170697","https://openalex.org/W3035326363","https://openalex.org/W3035626604","https://openalex.org/W3037212379","https://openalex.org/W3037805286","https://openalex.org/W3107992834","https://openalex.org/W6635084905","https://openalex.org/W6676039487","https://openalex.org/W6683401474","https://openalex.org/W6714644935","https://openalex.org/W6718140377","https://openalex.org/W6729203000","https://openalex.org/W6729623730","https://openalex.org/W6738543193","https://openalex.org/W6743531586","https://openalex.org/W6744627333","https://openalex.org/W6746124379","https://openalex.org/W6748223763","https://openalex.org/W6748391871","https://openalex.org/W6748582592","https://openalex.org/W6756031963","https://openalex.org/W6756040250","https://openalex.org/W6756053146","https://openalex.org/W6756663807","https://openalex.org/W6762124240","https://openalex.org/W6763594558","https://openalex.org/W6765554853","https://openalex.org/W6765779288","https://openalex.org/W6767337212","https://openalex.org/W6767644857","https://openalex.org/W6767869643","https://openalex.org/W6771946023","https://openalex.org/W6773517458","https://openalex.org/W6774817443","https://openalex.org/W6774958508"],"related_works":["https://openalex.org/W2947511876","https://openalex.org/W3034917202","https://openalex.org/W3025243508","https://openalex.org/W2999820041","https://openalex.org/W2796277480","https://openalex.org/W2996951986","https://openalex.org/W2895072925","https://openalex.org/W3120479086","https://openalex.org/W3091676835","https://openalex.org/W3096252355","https://openalex.org/W2964352424","https://openalex.org/W3111025744","https://openalex.org/W3176141297","https://openalex.org/W2951140085","https://openalex.org/W3204231799","https://openalex.org/W3169435634","https://openalex.org/W2989901151","https://openalex.org/W2895779420","https://openalex.org/W2895281799","https://openalex.org/W2809129535"],"abstract_inverted_index":{"Recent":[0],"generative":[1],"adversarial":[2],"networks":[3],"(GANs)":[4],"are":[5,97],"able":[6,149],"to":[7,36,54,79,139,150,204],"generate":[8],"impressive":[9],"photo-realistic":[10],"images.":[11],"However,":[12],"controllable":[13,23],"generation":[14,24],"with":[15,182,194],"GANs":[16],"remains":[17],"an":[18,51,69],"open":[19],"research":[20],"problem.":[21],"Achieving":[22],"requires":[25],"semantically":[26,152],"interpretable":[27],"and":[28,82,122,147,165],"disentangled":[29],"factors":[30],"of":[31,58,87,111,129,171,180],"variation.":[32],"It":[33],"is":[34,105,148],"challenging":[35],"achieve":[37],"this":[38],"goal":[39],"using":[40],"simple":[41],"fixed":[42],"distributions":[43],"such":[44,142],"as":[45,91,143],"Gaussian":[46],"distribution.":[47],"Instead,":[48],"we":[49],"propose":[50],"unsupervised":[52],"framework":[53,134],"learn":[55,123],"a":[56,100,116,178,183],"distribution":[57],"latent":[59,89,95,101,154],"codes":[60,90,96,155],"that":[61,104,168,197],"control":[62],"the":[63,73,77,80,85,88,108,112,130,144,174],"generator":[64],"through":[65,126],"self-training.":[66],"Self-training":[67],"provides":[68],"iterative":[70],"feedback":[71],"in":[72,107,173],"GAN":[74],"training,":[75],"from":[76,99],"discriminator":[78],"generator,":[81],"progressively":[83],"improves":[84],"proposal":[86],"training":[92],"proceeds.":[93],"The":[94],"sampled":[98],"variable":[102],"model":[103,121],"learned":[106,175],"feature":[109],"space":[110],"discriminator.":[113],"We":[114,159,191],"consider":[115],"normalized":[117],"independent":[118],"component":[119],"analysis":[120],"its":[124],"parameters":[125],"tensor":[127],"factorization":[128],"higher-order":[131],"moments.":[132],"Our":[133],"exhibits":[135],"better":[136,201],"disentanglement":[137],"compared":[138,203],"other":[140,205],"variants":[141],"variational":[145],"autoencoder,":[146],"discover":[151],"meaningful":[153],"without":[156],"any":[157],"supervision.":[158],"empirically":[160],"demonstrate":[161,193],"on":[162],"both":[163],"cars":[164],"faces":[166],"datasets":[167],"each":[169],"group":[170],"elements":[172],"code":[176],"controls":[177],"mode":[179],"variation":[181],"semantic":[184],"meaning,":[185],"e.g.":[186],"pose":[187],"or":[188],"background":[189],"change.":[190],"also":[192],"quantitative":[195],"metrics":[196],"our":[198],"method":[199],"generates":[200],"results":[202],"approaches.":[206]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}