{"id":"https://openalex.org/W3011199201","doi":"https://doi.org/10.1109/icassp40776.2020.9053146","title":"This Dataset Does Not Exist: Training Models from Generated Images","display_name":"This Dataset Does Not Exist: Training Models from Generated Images","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3011199201","doi":"https://doi.org/10.1109/icassp40776.2020.9053146","mag":"3011199201"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"preprint","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/A5074326665","display_name":"Victor Besnier","orcid":null},"institutions":[{"id":"https://openalex.org/I220619192","display_name":"Valeo (France)","ror":"https://ror.org/04ryqpf83","country_code":"FR","type":"company","lineage":["https://openalex.org/I220619192"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Victor Besnier","raw_affiliation_strings":["Valeo.ai, Paris, France"],"affiliations":[{"raw_affiliation_string":"Valeo.ai, Paris, France","institution_ids":["https://openalex.org/I220619192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060420342","display_name":"Himalaya Jain","orcid":"https://orcid.org/0000-0002-8932-7661"},"institutions":[{"id":"https://openalex.org/I220619192","display_name":"Valeo (France)","ror":"https://ror.org/04ryqpf83","country_code":"FR","type":"company","lineage":["https://openalex.org/I220619192"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Himalaya Jain","raw_affiliation_strings":["Valeo.ai, Paris, France"],"affiliations":[{"raw_affiliation_string":"Valeo.ai, Paris, France","institution_ids":["https://openalex.org/I220619192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003783466","display_name":"Andrei Bursuc","orcid":"https://orcid.org/0000-0003-2485-9402"},"institutions":[{"id":"https://openalex.org/I220619192","display_name":"Valeo (France)","ror":"https://ror.org/04ryqpf83","country_code":"FR","type":"company","lineage":["https://openalex.org/I220619192"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Andrei Bursuc","raw_affiliation_strings":["Valeo.ai, Paris, France"],"affiliations":[{"raw_affiliation_string":"Valeo.ai, Paris, France","institution_ids":["https://openalex.org/I220619192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108118084","display_name":"Matthieu Cord","orcid":"https://orcid.org/0000-0002-0627-5844"},"institutions":[{"id":"https://openalex.org/I220619192","display_name":"Valeo (France)","ror":"https://ror.org/04ryqpf83","country_code":"FR","type":"company","lineage":["https://openalex.org/I220619192"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Matthieu Cord","raw_affiliation_strings":["Valeo.ai, Paris, France"],"affiliations":[{"raw_affiliation_string":"Valeo.ai, Paris, France","institution_ids":["https://openalex.org/I220619192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113908330","display_name":"Patrick P\u00e9rez","orcid":null},"institutions":[{"id":"https://openalex.org/I220619192","display_name":"Valeo (France)","ror":"https://ror.org/04ryqpf83","country_code":"FR","type":"company","lineage":["https://openalex.org/I220619192"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Patrick Perez","raw_affiliation_strings":["Valeo.ai, Paris, France"],"affiliations":[{"raw_affiliation_string":"Valeo.ai, Paris, France","institution_ids":["https://openalex.org/I220619192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5074326665"],"corresponding_institution_ids":["https://openalex.org/I220619192"],"apc_list":null,"apc_paid":null,"fwci":2.72915027,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.91179926,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9944999814033508,"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/T10862","display_name":"AI in cancer detection","score":0.9929999709129333,"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":0.8308029770851135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7045612931251526},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.6973173022270203},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6868522763252258},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6455379128456116},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.618299126625061},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.576434850692749},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5670080184936523},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4725978970527649},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.42604056000709534},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34960800409317017}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8308029770851135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7045612931251526},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.6973173022270203},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6868522763252258},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6455379128456116},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.618299126625061},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.576434850692749},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5670080184936523},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4725978970527649},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.42604056000709534},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34960800409317017},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W2117539524","https://openalex.org/W2751049198","https://openalex.org/W2785678896","https://openalex.org/W2795144650","https://openalex.org/W2884805522","https://openalex.org/W2888026659","https://openalex.org/W2893749619","https://openalex.org/W2945027741","https://openalex.org/W2948425579","https://openalex.org/W2949117887","https://openalex.org/W2949813473","https://openalex.org/W2952716587","https://openalex.org/W2962879692","https://openalex.org/W2963211188","https://openalex.org/W2963373786","https://openalex.org/W2963552443","https://openalex.org/W2963836885","https://openalex.org/W2963981733","https://openalex.org/W2971337561","https://openalex.org/W2995801453","https://openalex.org/W3003162010","https://openalex.org/W3034431451","https://openalex.org/W3118608800","https://openalex.org/W4295521014","https://openalex.org/W4297818800","https://openalex.org/W4301206121","https://openalex.org/W4394670483","https://openalex.org/W6638667902","https://openalex.org/W6718379498","https://openalex.org/W6735913928","https://openalex.org/W6738045163","https://openalex.org/W6744093492","https://openalex.org/W6748582592","https://openalex.org/W6749904568","https://openalex.org/W6753904365","https://openalex.org/W6755312952","https://openalex.org/W6762987983","https://openalex.org/W6763309848","https://openalex.org/W6765645287","https://openalex.org/W6765779288","https://openalex.org/W6779669310","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2130553454","https://openalex.org/W3022007134","https://openalex.org/W4317548404","https://openalex.org/W2087783760","https://openalex.org/W2033364610","https://openalex.org/W3163689946","https://openalex.org/W2797776314","https://openalex.org/W2153927146","https://openalex.org/W2091066410","https://openalex.org/W3104108945"],"abstract_inverted_index":{"Current":[0],"generative":[1,12],"networks":[2],"are":[3],"increasingly":[4],"proficient":[5],"in":[6],"generating":[7],"high-resolution":[8],"realistic":[9],"images.":[10,153],"These":[11],"networks,":[13],"especially":[14],"the":[15,32,41,67,92,117,140],"conditional":[16],"ones,":[17],"can":[18,110],"potentially":[19],"become":[20],"a":[21,37,137],"great":[22],"tool":[23],"for":[24,57,69],"providing":[25],"new":[26],"image":[27],"datasets.":[28,74],"This":[29,44],"naturally":[30],"brings":[31],"question:":[33],"Can":[34],"we":[35,78],"train":[36],"classifier":[38],"only":[39],"on":[40,97,123,128,136,151],"generated":[42,99,124],"data?":[43],"potential":[45],"availability":[46],"of":[47,51,116,139],"nearly":[48],"unlimited":[49],"amounts":[50],"training":[52,58,96,122],"data":[53,120],"challenges":[54],"standard":[55],"practices":[56],"machine":[59],"learning":[60],"models,":[61],"which":[62],"have":[63],"been":[64],"crafted":[65],"across":[66],"years":[68],"limited":[70],"and":[71,82,126,143],"fixed":[72],"size":[73],"In":[75],"this":[76,80],"work":[77],"investigate":[79],"question":[81],"its":[83],"related":[84],"challenges.":[85],"We":[86,104,131],"identify":[87],"ways":[88],"to":[89,148],"improve":[90],"significantly":[91],"performance":[93],"over":[94],"naive":[95],"randomly":[98],"images":[100],"with":[101],"regular":[102],"heuristics.":[103],"propose":[105],"three":[106],"standalone":[107],"techniques":[108],"that":[109],"be":[111],"applied":[112],"at":[113],"different":[114],"stages":[115],"pipeline,":[118],"i.e.,":[119],"generation,":[121],"data,":[125],"deploying":[127],"real":[129,152],"data.":[130],"evaluate":[132],"our":[133],"proposed":[134],"approaches":[135],"subset":[138],"ImageNet":[141],"dataset":[142],"show":[144],"encouraging":[145],"results":[146],"compared":[147],"classifiers":[149],"trained":[150]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-02-20T08:17:22.645390","created_date":"2025-10-10T00:00:00"}