{"id":"https://openalex.org/W4409263028","doi":"https://doi.org/10.1109/wacv61041.2025.00351","title":"Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models","display_name":"Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models","publication_year":2025,"publication_date":"2025-02-26","ids":{"openalex":"https://openalex.org/W4409263028","doi":"https://doi.org/10.1109/wacv61041.2025.00351"},"language":"en","primary_location":{"id":"doi:10.1109/wacv61041.2025.00351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61041.2025.00351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102703554","display_name":"Youngjun Jun","orcid":null},"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"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngjun Jun","raw_affiliation_strings":["Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051553362","display_name":"Ki Duk Park","orcid":"https://orcid.org/0000-0002-7753-214X"},"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"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jiwoo Park","raw_affiliation_strings":["Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089745396","display_name":"Kyobin Choo","orcid":"https://orcid.org/0000-0003-2856-402X"},"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"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyobin Choo","raw_affiliation_strings":["Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114635597","display_name":"Tae Eun Choi","orcid":null},"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"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Tae Eun Choi","raw_affiliation_strings":["Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022991142","display_name":"Seong Jae Hwang","orcid":"https://orcid.org/0000-0002-3713-5553"},"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"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seong Jae Hwang","raw_affiliation_strings":["Yonsei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3559","last_page":"3569"},"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":0.8511999845504761,"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":0.8511999845504761,"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/T10028","display_name":"Topic Modeling","score":0.821399986743927,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.7752000093460083,"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/computer-science","display_name":"Computer science","score":0.664620041847229},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5773527026176453},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0741928219795227},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.05548587441444397}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.664620041847229},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5773527026176453},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0741928219795227},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.05548587441444397}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61041.2025.00351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61041.2025.00351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.800000011920929,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W1770887089","https://openalex.org/W1834627138","https://openalex.org/W2095373758","https://openalex.org/W2108876167","https://openalex.org/W2163922914","https://openalex.org/W2620364083","https://openalex.org/W2903141607","https://openalex.org/W2919115771","https://openalex.org/W2963305465","https://openalex.org/W3014852036","https://openalex.org/W3177221875","https://openalex.org/W3217030260","https://openalex.org/W4213251304","https://openalex.org/W4285794641","https://openalex.org/W4308673310","https://openalex.org/W4312933868","https://openalex.org/W4385569875","https://openalex.org/W4386072096","https://openalex.org/W4390873054","https://openalex.org/W4393153872","https://openalex.org/W4400188660","https://openalex.org/W4402715897","https://openalex.org/W4402716152","https://openalex.org/W6639824700","https://openalex.org/W6640963894","https://openalex.org/W6674330103","https://openalex.org/W6678947187","https://openalex.org/W6683307983","https://openalex.org/W6686131816","https://openalex.org/W6718140377","https://openalex.org/W6738560588","https://openalex.org/W6744627333","https://openalex.org/W6745687250","https://openalex.org/W6747786891","https://openalex.org/W6748223763","https://openalex.org/W6748391871","https://openalex.org/W6748445624","https://openalex.org/W6750852989","https://openalex.org/W6756663807","https://openalex.org/W6756824971","https://openalex.org/W6763012920","https://openalex.org/W6765779288","https://openalex.org/W6773517458","https://openalex.org/W6779459370","https://openalex.org/W6779823529","https://openalex.org/W6783713337","https://openalex.org/W6790546009","https://openalex.org/W6803036039","https://openalex.org/W6838452192","https://openalex.org/W6838617955","https://openalex.org/W6838907741","https://openalex.org/W6840479559","https://openalex.org/W6846143867","https://openalex.org/W6846344700","https://openalex.org/W6848596214","https://openalex.org/W6849606306","https://openalex.org/W6849791629","https://openalex.org/W6853280509","https://openalex.org/W6854511533","https://openalex.org/W6857461762","https://openalex.org/W6861214084","https://openalex.org/W6865158897"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Disentangled":[0],"representation":[1],"learning":[2],"(DRL)":[3],"aims":[4],"to":[5,63,70,86,127],"break":[6],"down":[7],"observed":[8],"data":[9],"into":[10],"core":[11],"intrinsic":[12],"factors":[13,29],"for":[14,54,91],"a":[15],"profound":[16],"understanding":[17],"of":[18,42,157],"the":[19,71,101,109,124,136,145,150,155],"data.":[20],"In":[21,78],"real-world":[22],"scenarios,":[23],"manually":[24],"defining":[25],"and":[26,149,168],"labeling":[27],"these":[28],"are":[30,48],"non-trivial,":[31],"making":[32],"unsupervised":[33,55],"methods":[34],"attractive.":[35],"Recently,":[36],"there":[37],"have":[38],"been":[39],"limited":[40],"explorations":[41],"utilizing":[43],"diffusion":[44],"models":[45],"(DMs),":[46],"which":[47,121,142],"already":[49],"mainstream":[50],"in":[51,175],"generative":[52],"modeling,":[53],"DRL.":[56,94],"They":[57],"implement":[58],"their":[59],"own":[60],"inductive":[61,97],"bias":[62,98],"ensure":[64],"that":[65],"each":[66],"latent":[67,89,112,146],"unit":[68,147],"input":[69],"DM":[72,152],"expresses":[73],"only":[74],"one":[75],"distinct":[76,151],"factor.":[77],"this":[79],"context,":[80],"we":[81,115],"design":[82],"Dynamic":[83],"Gaussian":[84],"Anchoring":[85],"enforce":[87],"attribute-separated":[88],"units":[90],"more":[92,129],"interpretable":[93],"This":[95],"unconventional":[96],"explicitly":[99],"delineates":[100],"decision":[102],"boundaries":[103],"between":[104],"attributes":[105],"while":[106],"also":[107,116],"promoting":[108],"independence":[110],"among":[111],"units.":[113],"Additionally,":[114],"propose":[117],"Skip":[118],"Dropout":[119],"technique,":[120],"easily":[122],"modifies":[123],"denoising":[125],"U-Net":[126],"be":[128],"DRL-friendly,":[130],"addressing":[131],"its":[132],"uncooperative":[133],"nature":[134],"with":[135],"disentangling":[137],"feature":[138],"extractor.":[139],"Our":[140],"methods,":[141],"carefully":[143],"consider":[144],"semantics":[148],"structure,":[153],"enhance":[154],"practicality":[156],"DM-based":[158],"disentangled":[159],"representations,":[160],"demonstrating":[161],"state-of-the-art":[162],"disentanglement":[163],"performance":[164],"on":[165],"both":[166],"synthetic":[167],"real":[169],"data,":[170],"as":[171,173],"well":[172],"advantages":[174],"downstream":[176],"tasks.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
