{"id":"https://openalex.org/W4221141695","doi":"https://doi.org/10.24963/ijcai.2022/313","title":"Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs","display_name":"Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4221141695","doi":"https://doi.org/10.24963/ijcai.2022/313"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2022/313","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/313","pdf_url":"https://www.ijcai.org/proceedings/2022/0313.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2022/0313.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066652469","display_name":"Bowen Tian","orcid":"https://orcid.org/0000-0003-1945-9631"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Tian","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043729465","display_name":"Qinliang Su","orcid":"https://orcid.org/0000-0002-5903-2504"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210092257","display_name":"China Guangzhou Analysis and Testing Center","ror":"https://ror.org/00d6bjs95","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210092257"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinliang Su","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","institution_ids":["https://openalex.org/I4210092257"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017205177","display_name":"Jian Yin","orcid":"https://orcid.org/0000-0002-1214-5384"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210092257","display_name":"China Guangzhou Analysis and Testing Center","ror":"https://ror.org/00d6bjs95","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210092257"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yin","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","School of Artificial Intelligence, Sun Yat-sen University, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","institution_ids":["https://openalex.org/I4210092257"]},{"raw_affiliation_string":"School of Artificial Intelligence, Sun Yat-sen University, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2255","last_page":"2261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.8522577285766602},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.7773680686950684},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6829949021339417},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6051306128501892},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41808727383613586},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33775150775909424},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10455292463302612}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8522577285766602},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.7773680686950684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6829949021339417},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6051306128501892},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41808727383613586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33775150775909424},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10455292463302612},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2022/313","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/313","pdf_url":"https://www.ijcai.org/proceedings/2022/0313.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2022/313","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/313","pdf_url":"https://www.ijcai.org/proceedings/2022/0313.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2284794279","display_name":null,"funder_award_id":"61806223","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6840496190","display_name":null,"funder_award_id":"U1811264","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7276073139","display_name":null,"funder_award_id":"2021A1515012299","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4221141695.pdf","grobid_xml":"https://content.openalex.org/works/W4221141695.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W1970088130","https://openalex.org/W2108501770","https://openalex.org/W2132870739","https://openalex.org/W2142889610","https://openalex.org/W2143559571","https://openalex.org/W2204904589","https://openalex.org/W2398119937","https://openalex.org/W2533835508","https://openalex.org/W2593414223","https://openalex.org/W2599354622","https://openalex.org/W2752660961","https://openalex.org/W2786088545","https://openalex.org/W2787947370","https://openalex.org/W2803697594","https://openalex.org/W2902758299","https://openalex.org/W2962808998","https://openalex.org/W2963045681","https://openalex.org/W2963049059","https://openalex.org/W2987228832","https://openalex.org/W3040266635","https://openalex.org/W3044735541","https://openalex.org/W3127929528","https://openalex.org/W3184778778","https://openalex.org/W3188545799","https://openalex.org/W4288335160","https://openalex.org/W4294568686","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"The":[0],"goal":[1],"of":[2,19,45,62,137,173],"anomaly":[3,40,46,53,138],"detection":[4,139],"is":[5,107,121,148,167],"to":[6,23,34,73,89,112,123,133,150,169,179,184],"identify":[7],"anomalous":[8,64,130,176],"samples":[9,116],"from":[10,38],"normal":[11,84,115],"ones.":[12],"In":[13],"this":[14,60,99],"paper,":[15],"a":[16,75],"small":[17],"number":[18],"anomalies":[20],"are":[21,32],"assumed":[22,33],"be":[24,35,151],"available":[25],"at":[26,55],"the":[27,43,51,68,83,95,114,135,144,157,174],"training":[28],"stage,":[29],"but":[30,86],"they":[31],"collected":[36,52,69,96,129],"only":[37,81],"several":[39],"types,":[41],"leaving":[42],"majority":[44],"types":[47],"not":[48,80],"represented":[49,66],"in":[50,110],"dataset":[54],"all.":[56],"To":[57,98],"effectively":[58,170],"leverage":[59],"kind":[61],"incomplete":[63,175],"knowledge":[65],"by":[67],"anomalies,":[70],"we":[71],"propose":[72],"learn":[74],"probability":[76],"distribution":[77],"that":[78,163],"can":[79],"model":[82],"samples,":[85],"also":[87],"guarantee":[88],"assign":[90],"low":[91],"density":[92],"values":[93],"for":[94,128,156],"anomalies.":[97],"end,":[100],"an":[101,154],"anomaly-aware":[102,146],"generative":[103],"adversarial":[104],"network":[105],"(GAN)":[106],"developed,":[108],"which,":[109],"addition":[111],"modeling":[113],"as":[117],"most":[118],"GANs":[119],"do,":[120],"able":[122,168],"explicitly":[124],"avoid":[125],"assigning":[126],"probabilities":[127],"samples.":[131],"Moreover,":[132],"facilitate":[134],"computation":[136],"criteria":[140],"like":[141],"reconstruction":[142],"error,":[143],"proposed":[145,165],"GAN":[147],"designed":[149],"bidirectional,":[152],"attaching":[153],"encoder":[155],"generator.":[158],"Extensive":[159],"experimental":[160],"results":[161],"demonstrate":[162],"our":[164],"method":[166],"make":[171],"use":[172],"information,":[177],"leading":[178],"significant":[180],"performance":[181],"gains":[182],"comparing":[183],"existing":[185],"methods.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}