{"id":"https://openalex.org/W3203520239","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534262","title":"Adversarial Imbalance Classification with Class-Specific Diverse Instance Generation","display_name":"Adversarial Imbalance Classification with Class-Specific Diverse Instance Generation","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3203520239","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534262","mag":"3203520239"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534262","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":"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/A5004472639","display_name":"Qihang Shen","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":true,"raw_author_name":"Qihang Shen","raw_affiliation_strings":["Beijing Jiaotong University,Beijing,China","Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","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":false,"raw_author_name":"Xinyue Wang","raw_affiliation_strings":["Beijing Jiaotong University,Beijing,China","Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101133571","display_name":"Zixin Cai","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":"Zixin Cai","raw_affiliation_strings":["Beijing Jiaotong University,Beijing,China","Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"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 Jiaotong University,Beijing,China","Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004472639"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14063134,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"49","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9983000159263611,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9944000244140625,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9847000241279602,"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/overfitting","display_name":"Overfitting","score":0.9135605096817017},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8387535810470581},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.8216133117675781},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7571936249732971},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7133167386054993},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6842322945594788},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.47881990671157837},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47446876764297485},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4408615827560425},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3329521119594574},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1662428379058838},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12301942706108093}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9135605096817017},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8387535810470581},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.8216133117675781},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7571936249732971},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7133167386054993},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6842322945594788},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.47881990671157837},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47446876764297485},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4408615827560425},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3329521119594574},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1662428379058838},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12301942706108093}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534262","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2448028817","display_name":null,"funder_award_id":"61822601,61773050,61632004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G600048247","display_name":null,"funder_award_id":"Z180006","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G6498984230","display_name":null,"funder_award_id":"2020AAA0106800,2017YFC1703506","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8377820436","display_name":null,"funder_award_id":"2019JBZ110","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1563938718","https://openalex.org/W1834627138","https://openalex.org/W1842879116","https://openalex.org/W1959608418","https://openalex.org/W1993220166","https://openalex.org/W2071072418","https://openalex.org/W2099471712","https://openalex.org/W2099550922","https://openalex.org/W2104167780","https://openalex.org/W2104933073","https://openalex.org/W2106479238","https://openalex.org/W2118978333","https://openalex.org/W2132791018","https://openalex.org/W2148143831","https://openalex.org/W2335728318","https://openalex.org/W2490420619","https://openalex.org/W2508932077","https://openalex.org/W2562319768","https://openalex.org/W2623550831","https://openalex.org/W2750384547","https://openalex.org/W2767106145","https://openalex.org/W2793167745","https://openalex.org/W2795144650","https://openalex.org/W2952448206","https://openalex.org/W2963351448","https://openalex.org/W2963746531","https://openalex.org/W2979159783","https://openalex.org/W2981515171","https://openalex.org/W3020579052","https://openalex.org/W3034561829","https://openalex.org/W3041664846","https://openalex.org/W3092707779","https://openalex.org/W4244036394","https://openalex.org/W4320013936","https://openalex.org/W6640963894","https://openalex.org/W6664464457","https://openalex.org/W6675634716","https://openalex.org/W6725434214","https://openalex.org/W6743688258","https://openalex.org/W6745535286","https://openalex.org/W6749904568"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W3128011703","https://openalex.org/W2005234362","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Data":[0],"augmentation":[1,19,34],"is":[2,60,64,107],"an":[3,50],"effective":[4],"technique":[5],"for":[6],"imbalance":[7,169],"classification.":[8],"However,":[9],"it":[10],"still":[11],"suffers":[12],"from":[13,32],"two":[14,79],"key":[15],"issues.":[16],"Firstly,":[17],"data":[18],"and":[20,72,126,146,164],"classifier":[21,27,70,90,131],"construction":[22,28,71],"are":[23],"performed":[24],"separately,":[25],"where":[26,77],"may":[29,42],"not":[30],"benefit":[31],"the":[33,92,102,129,150],"strategies.":[35],"Secondly,":[36],"low":[37],"variations":[38],"in":[39],"generated":[40],"instances":[41,96,119],"lead":[43],"to":[44,109,116],"overfitting":[45,163],"problem.":[46],"In":[47],"this":[48],"paper,":[49],"Adversarial":[51],"Imbalance":[52],"Classification":[53],"method":[54,159],"with":[55,120,149],"Class-specific":[56],"Diverse":[57],"Instance":[58],"Generation":[59],"proposed":[61],"(AICIG).":[62],"It":[63],"a":[65,86,111],"framework":[66],"that":[67,157],"unifies":[68],"adversarial":[69,89,99,130],"class-specific":[73,95,98,125],"diverse":[74,118,127],"instance":[75],"generation,":[76],"these":[78],"stages":[80],"enforce":[81],"each":[82],"other":[83],"seamlessly.":[84],"Concretely,":[85],"specially":[87],"designed":[88],"drives":[91],"generation":[93],"of":[94,104],"by":[97],"process.":[100],"Meanwhile,":[101],"distribution":[103],"latent":[105],"variables":[106],"assumed":[108],"be":[110],"Gaussian":[112],"mixture,":[113],"which":[114],"enables":[115],"generate":[117],"limited":[121],"data.":[122],"With":[123],"augmented":[124],"instances,":[128],"can":[132,160],"obtain":[133,165],"better":[134,166],"generalization":[135],"performance.":[136],"We":[137],"conduct":[138],"experiments":[139],"on":[140,168],"four":[141],"widely-used":[142],"imbalanced":[143],"image":[144],"datasets":[145],"compare":[147],"them":[148],"state-of-the-art":[151],"methods.":[152],"The":[153],"experimental":[154],"results":[155],"exhibit":[156],"our":[158],"effectively":[161],"prevent":[162],"performance":[167],"classification":[170],"tasks.":[171]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}