{"id":"https://openalex.org/W3034561829","doi":"https://doi.org/10.1109/cvpr42600.2020.01413","title":"Deep Generative Model for Robust Imbalance Classification","display_name":"Deep Generative Model for Robust Imbalance Classification","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3034561829","doi":"https://doi.org/10.1109/cvpr42600.2020.01413","mag":"3034561829"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr42600.2020.01413","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr42600.2020.01413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"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/A5100408892","display_name":"Xinyue Wang","orcid":"https://orcid.org/0009-0005-3851-5768"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyue Wang","raw_affiliation_strings":["Beijing Key Lab of Traffic Data AnaLysis and Mining, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data AnaLysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054177342","display_name":"Yilin Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilin Lyu","raw_affiliation_strings":["Beijing Key Lab of Traffic Data AnaLysis and Mining, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data AnaLysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069749738","display_name":"Liping Jing","orcid":"https://orcid.org/0000-0001-7578-3407"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Jing","raw_affiliation_strings":["Beijing Key Lab of Traffic Data AnaLysis and Mining, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data AnaLysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100408892"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":2.2577,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.90048261,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"14112","last_page":"14121"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9991999864578247,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9991999864578247,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9990000128746033,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.72886723279953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7089130282402039},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6317154169082642},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6076292991638184},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6058571338653564},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6027461290359497},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5957667827606201},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.43010270595550537},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4211012125015259},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41663622856140137},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40319252014160156}],"concepts":[{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.72886723279953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7089130282402039},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6317154169082642},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6076292991638184},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6058571338653564},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6027461290359497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5957667827606201},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.43010270595550537},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4211012125015259},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41663622856140137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40319252014160156},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr42600.2020.01413","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr42600.2020.01413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W930928758","https://openalex.org/W1834627138","https://openalex.org/W1941659294","https://openalex.org/W1959608418","https://openalex.org/W2023847169","https://openalex.org/W2118978333","https://openalex.org/W2124685890","https://openalex.org/W2187089797","https://openalex.org/W2335728318","https://openalex.org/W2490420619","https://openalex.org/W2508932077","https://openalex.org/W2551446786","https://openalex.org/W2562319768","https://openalex.org/W2604968786","https://openalex.org/W2617539464","https://openalex.org/W2739748921","https://openalex.org/W2750384547","https://openalex.org/W2756182389","https://openalex.org/W2765622255","https://openalex.org/W2786532017","https://openalex.org/W2795144650","https://openalex.org/W2804078698","https://openalex.org/W2807992610","https://openalex.org/W2809316507","https://openalex.org/W2906792045","https://openalex.org/W2910171846","https://openalex.org/W2951266961","https://openalex.org/W2952745707","https://openalex.org/W2963226019","https://openalex.org/W2963351448","https://openalex.org/W2963469976","https://openalex.org/W2963563295","https://openalex.org/W2963746531","https://openalex.org/W2963765036","https://openalex.org/W2964110122","https://openalex.org/W2966095869","https://openalex.org/W2981515171","https://openalex.org/W4289287853","https://openalex.org/W4297795851","https://openalex.org/W4298325296","https://openalex.org/W4320013936","https://openalex.org/W6624452987","https://openalex.org/W6718140377","https://openalex.org/W6734209382","https://openalex.org/W6741832134","https://openalex.org/W6745535286","https://openalex.org/W6749904568","https://openalex.org/W6751794319","https://openalex.org/W6764214684"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W2461917396","https://openalex.org/W1966667550","https://openalex.org/W2037497866","https://openalex.org/W4243467573","https://openalex.org/W2616125534","https://openalex.org/W2770703741","https://openalex.org/W2963987720","https://openalex.org/W2146310005"],"abstract_inverted_index":{"Discovering":[0],"hidden":[1],"pattern":[2],"from":[3,23],"imbalanced":[4,156],"data":[5,27,55,129,146],"is":[6,47,65,90,106],"a":[7,43,68,93,101,123],"critical":[8],"issue":[9,52],"in":[10,34,142],"various":[11],"real-world":[12],"applications":[13],"including":[14],"computer":[15],"vision.":[16],"The":[17],"existing":[18],"classification":[19,176],"methods":[20],"usually":[21],"suffer":[22],"the":[24,29,61,74,80,87,126,162,168],"limitation":[25],"of":[26,83,111,128,170],"especially":[28],"minority":[30],"classes,":[31],"and":[32,37,57,113,144],"result":[33],"unstable":[35],"prediction":[36],"low":[38],"performance.":[39],"In":[40],"this":[41,51,119],"paper,":[42],"deep":[44,69],"generative":[45,63],"classifier":[46,64],"proposed":[48,62,172],"to":[49,78,108,115],"mitigate":[50],"via":[53],"both":[54],"perturbation":[56],"model":[58,72,112,173],"perturbation.":[59,147],"Specially,":[60],"modeled":[66],"by":[67,92],"latent":[70,75,88,120],"variable":[71,76,89],"where":[73],"aims":[77],"capture":[79],"direct":[81],"cause":[82],"target":[84],"label.":[85],"Meanwhile,":[86],"represented":[91],"probability":[94],"distribution":[95],"over":[96],"possible":[97],"values":[98],"rather":[99],"than":[100],"single":[102],"fixed":[103],"value,":[104],"which":[105],"able":[107],"enforce":[109],"uncertainty":[110],"lead":[114],"stable":[116],"prediction.":[117],"Furthermore,":[118],"variable,":[121],"as":[122],"confounder,":[124],"affects":[125],"process":[127],"(feature/label)":[130],"generation,":[131],"so":[132],"that":[133],"we":[134],"can":[135],"arrive":[136],"at":[137],"well-justified":[138],"sampling":[139],"variability":[140],"considerations":[141],"statistics,":[143],"implement":[145],"Extensive":[148],"experiments":[149],"have":[150],"been":[151],"conducted":[152],"on":[153,174],"widely-used":[154],"real":[155],"image":[157],"datasets.":[158],"By":[159],"comparing":[160],"with":[161],"state-of-the-art":[163],"methods,":[164],"experimental":[165],"results":[166],"demonstrate":[167],"superiority":[169],"our":[171],"imbalance":[175],"task.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}
