{"id":"https://openalex.org/W3082744875","doi":null,"title":"Metrics for Exposing the Biases of Content-Style Disentanglement","display_name":"Metrics for Exposing the Biases of Content-Style Disentanglement","publication_year":2020,"publication_date":"2020-08-27","ids":{"openalex":"https://openalex.org/W3082744875","mag":"3082744875"},"language":"en","primary_location":{"id":"mag:3082744875","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2008.12378","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,"listed_in":[],"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},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.12378","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101489106","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0001-5430-4032","observed_orcids":["https://orcid.org/0000-0001-5430-4032"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015117681","display_name":"Spyridon Thermos","orcid":"https://orcid.org/0000-0001-5458-6906","observed_orcids":["https://orcid.org/0000-0001-5458-6906"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spyridon Thermos","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5458-6906","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042576093","display_name":"Gabriele Valvano","orcid":"https://orcid.org/0000-0002-4764-2997","observed_orcids":["https://orcid.org/0000-0002-4764-2997"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gabriele Valvano","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4764-2997","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056737139","display_name":"Agisilaos Chartsias","orcid":"https://orcid.org/0000-0002-3512-5247","observed_orcids":["https://orcid.org/0000-0002-3512-5247"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agisilaos Chartsias","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3512-5247","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050825546","display_name":"Alison Q. O\u2019Neil","orcid":"https://orcid.org/0000-0001-8371-0603","observed_orcids":["https://orcid.org/0000-0001-8371-0603"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alison Q. O\u2019Neil","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8371-0603","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5054437456","display_name":"Sotirios A. Tsaftaris","orcid":"https://orcid.org/0000-0002-8795-9294","observed_orcids":["https://orcid.org/0000-0002-8795-9294"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sotirios A. Tsaftaris","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8795-9294","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9976000189781189,"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":0.9976000189781189,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0012000000569969416,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0006000000284984708,"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/content-style-disentanglement","display_name":"content-style disentanglement","score":0.9269999861717224},{"id":"https://openalex.org/keywords/latent-space-interpretability","display_name":"latent space interpretability","score":0.7710000276565552},{"id":"https://openalex.org/keywords/learning-objectives","display_name":"learning objectives","score":0.6000000238418579},{"id":"https://openalex.org/keywords/image-to-image-translation","display_name":"image-to-image translation","score":0.8560000061988831},{"id":"https://openalex.org/keywords/unsupervised-representation-learning","display_name":"unsupervised feature learning","score":0.6940000057220459},{"id":"https://openalex.org/keywords/ablation-study","display_name":"ablation study","score":0.49300000071525574}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7433812618255615},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7044399976730347},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.629496157169342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5453861951828003},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.45279017090797424},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4468739628791809},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.43801599740982056},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41128775477409363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3484981060028076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2163068950176239},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3082744875","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2008.12378","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,"listed_in":[],"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}],"best_oa_location":{"id":"mag:3082744875","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2008.12378","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,"listed_in":[],"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},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1638081485","https://openalex.org/W1691728462","https://openalex.org/W1786904711","https://openalex.org/W1959608418","https://openalex.org/W1987869189","https://openalex.org/W2134352661","https://openalex.org/W2142387771","https://openalex.org/W2157617585","https://openalex.org/W2163922914","https://openalex.org/W2188956040","https://openalex.org/W2242818861","https://openalex.org/W2336525740","https://openalex.org/W2431874326","https://openalex.org/W2471768434","https://openalex.org/W2502312327","https://openalex.org/W2557579533","https://openalex.org/W2592480533","https://openalex.org/W2611775752","https://openalex.org/W2615413256","https://openalex.org/W2621357189","https://openalex.org/W2753738274","https://openalex.org/W2760103357","https://openalex.org/W2785961484","https://openalex.org/W2786385136","https://openalex.org/W2787273002","https://openalex.org/W2796093898","https://openalex.org/W2797046819","https://openalex.org/W2804047627","https://openalex.org/W2902476877","https://openalex.org/W2904367110","https://openalex.org/W2922461432","https://openalex.org/W2951697117","https://openalex.org/W2953139137","https://openalex.org/W2959170286","https://openalex.org/W2962785568","https://openalex.org/W2962974533","https://openalex.org/W2963890275","https://openalex.org/W2963981733","https://openalex.org/W2964121744","https://openalex.org/W2981628132","https://openalex.org/W2982078832","https://openalex.org/W2996906101","https://openalex.org/W3001909981","https://openalex.org/W3014486277","https://openalex.org/W3032910416","https://openalex.org/W3101956722","https://openalex.org/W3139419286","https://openalex.org/W3147222900","https://openalex.org/W3165990540","https://openalex.org/W3189066069"],"related_works":["https://openalex.org/W4411245136","https://openalex.org/W4321471346","https://openalex.org/W3206708862","https://openalex.org/W3176220286","https://openalex.org/W3174272772","https://openalex.org/W6888004631","https://openalex.org/W2955042981","https://openalex.org/W4400434059","https://openalex.org/W3008944207","https://openalex.org/W4362679363"],"abstract_inverted_index":{"A":[0],"recent":[1],"spate":[2],"of":[3,30,97,101,153,183,190],"state-of-the-art":[4],"semi-":[5],"and":[6,21,40,50,82,104,108,112,129,140,166],"unsupervised":[7],"solutions":[8,32],"for":[9,37,79],"challenging":[10],"computer":[11],"vision":[12],"tasks":[13,59],"encode":[14],"image":[15,22],"content":[81,107],"into":[17,26],"a":[18,27,159],"spatial":[19,80],"tensor":[20],"appearance":[23],"or":[24,187],"style":[84,109],"vector.":[28],"Most":[29],"these":[31],"use":[33,146],"the":[34,95,106,151,181,188],"term":[35],"disentangled":[36],"their":[38],"representations":[39,110,206],"employ":[41,137],"different":[42],"biases":[43],"such":[44,91,193],"as":[45],"model":[46],"design,":[47],"learning":[48,130],"objectives,":[49],"data,":[51],"to":[52,69,93,118,149],"achieve":[53],"good":[54],"performance":[55,165],"in":[56,72,99,201],"spatially":[57],"equivariant":[58],"(e.g.":[60],"image-to-image":[61],"translation).":[62],"While":[63],"considerable":[64],"effort":[65],"has":[66],"been":[67],"made":[68],"measure":[70],"disentanglement":[71,98,139],"vector":[73,83],"representations,":[74],"we":[75,89,113,123,145],"have":[76],"lacked":[77],"metrics":[78,92,148,171],"representations.":[85],"In":[86,121],"this":[87,195],"paper,":[88],"propose":[90],"characterize":[94],"degree":[96],"terms":[100],"how":[102],"(un)correlated":[103],"informative":[105],"are,":[111],"further":[114],"examine":[115],"its":[116],"relation":[117],"task":[119,164,203],"performance.":[120],"particular,":[122],"first":[124],"identify":[125],"key":[126],"design":[127,182],"choices":[128],"constraints":[131],"on":[132],"three":[133],"popular":[134],"models":[135,186,192],"that":[136,194],"content-style":[138,205],"derive":[141],"ablated":[142],"versions.":[143],"Secondly,":[144],"our":[147],"ascertain":[150],"role":[152],"each":[154],"bias.":[155],"Our":[156,170],"experiments":[157],"reveal":[158],"sweet":[160,197],"spot":[161,198],"between":[162],"disentanglement,":[163],"latent":[167],"space":[168],"interpretability.":[169],"are":[172,207],"not":[173],"task-dependent;":[174],"thus,":[175],"they":[176],"can":[177],"help":[178],"guide":[179],"either":[180],"new":[184],"future":[185],"selection":[189],"viable":[191],"ideal":[196],"is":[199],"achieved":[200],"any":[202],"where":[204],"useful.":[208]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2020-09-08T00:00:00"}
