{"id":"https://openalex.org/W3200516021","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534019","title":"Incorporating Decision-level Reconstruction Quality in Adversarial Autoencoders for Anomaly Detection","display_name":"Incorporating Decision-level Reconstruction Quality in Adversarial Autoencoders for Anomaly Detection","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200516021","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534019","mag":"3200516021"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534019","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":"conference-paper","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/A5102158693","display_name":"Luyuan Li","orcid":null},"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":"Luyuan Li","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":"last","author":{"id":"https://openalex.org/A5085704922","display_name":"J. Andy","orcid":"https://orcid.org/0000-0002-0165-8416"},"institutions":[{"id":"https://openalex.org/I1327237609","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934","country_code":"CN","type":"government","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210127390"]},{"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":"Andy J Ma","raw_affiliation_strings":["Ministry of Education, Key Laboratory of Machine Intelligence and Advanced Computing, China","School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ministry of Education, Key Laboratory of Machine Intelligence and Advanced Computing, China","institution_ids":["https://openalex.org/I1327237609"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11950854,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9943000078201294,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9613999724388123,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7785341739654541},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7488786578178406},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7484386563301086},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7084771394729614},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6564199924468994},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6067069172859192},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5692735314369202},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5436261892318726},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5334230661392212},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4734888970851898},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.374856173992157},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.30270057916641235}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7785341739654541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7488786578178406},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7484386563301086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7084771394729614},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6564199924468994},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6067069172859192},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5692735314369202},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5436261892318726},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5334230661392212},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4734888970851898},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.374856173992157},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30270057916641235},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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:10.1109/ijcnn52387.2021.9534019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534019","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/G783529363","display_name":null,"funder_award_id":"61906218","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1823976324","https://openalex.org/W1959608418","https://openalex.org/W1970088130","https://openalex.org/W2099471712","https://openalex.org/W2103914106","https://openalex.org/W2127979711","https://openalex.org/W2132870739","https://openalex.org/W2136655611","https://openalex.org/W2145962650","https://openalex.org/W2155272489","https://openalex.org/W2173520492","https://openalex.org/W2204904589","https://openalex.org/W2398119937","https://openalex.org/W2599354622","https://openalex.org/W2740802195","https://openalex.org/W2750384547","https://openalex.org/W2780495118","https://openalex.org/W2786088545","https://openalex.org/W2786599352","https://openalex.org/W2803697594","https://openalex.org/W2860343088","https://openalex.org/W2905867642","https://openalex.org/W2911672077","https://openalex.org/W2914570111","https://openalex.org/W2925312408","https://openalex.org/W2948982773","https://openalex.org/W2949982377","https://openalex.org/W2963045681","https://openalex.org/W2963049059","https://openalex.org/W2963061824","https://openalex.org/W2963344330","https://openalex.org/W2963684088","https://openalex.org/W2977602779","https://openalex.org/W2978971541","https://openalex.org/W2982519139","https://openalex.org/W2987228832","https://openalex.org/W2997229301","https://openalex.org/W2997541219","https://openalex.org/W3016045843","https://openalex.org/W3034292309","https://openalex.org/W3035424058","https://openalex.org/W3090228265","https://openalex.org/W3100054152","https://openalex.org/W3106257921","https://openalex.org/W3118608800","https://openalex.org/W4320013936","https://openalex.org/W6640963894","https://openalex.org/W6679997575","https://openalex.org/W6685352114","https://openalex.org/W6712574313","https://openalex.org/W6743688258","https://openalex.org/W6748102297","https://openalex.org/W6751494907","https://openalex.org/W6753123669","https://openalex.org/W6774611950","https://openalex.org/W6776451990"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W4321789545","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037"],"abstract_inverted_index":{"Anomaly":[0],"detection,":[1,170],"also":[2,102],"known":[3],"as":[4],"one-class":[5],"classification,":[6],"is":[7,29,48,92],"a":[8,71,136,173],"challenging":[9],"problem":[10],"due":[11],"to":[12,30,51,94,118],"the":[13,22,39,86,110,140],"absence":[14],"of":[15,21,144,176],"abnormal":[16],"data":[17],"for":[18,25,34,134,168],"training.":[19],"One":[20],"promising":[23],"approaches":[24],"image":[26],"anomaly":[27,35,46,73,169],"detection":[28,74],"employ":[31],"adversarial":[32,90],"autoencoders":[33],"measurement":[36],"by":[37,76,159],"computing":[38],"pixel-level":[40,45,82,98,165],"reconstruction":[41,56,79,83,99,124,142,162,166],"errors.":[42],"However,":[43],"such":[44],"measure":[47,97],"very":[49],"sensitive":[50],"individual":[52],"pixels":[53],"with":[54,81,122,130,164],"huge":[55],"difference,":[57],"resulting":[58],"in":[59],"large":[60],"errors":[61],"even":[62],"on":[63,148],"normal":[64,107],"images.":[65],"In":[66,85],"this":[67],"paper,":[68],"we":[69],"propose":[70],"novel":[72],"method":[75,155],"combining":[77],"decision-level":[78,141,161],"quality":[80,125,132,143,163],"error.":[84],"proposed":[87],"method,":[88],"an":[89],"autoencoder":[91],"trained":[93],"not":[95],"only":[96],"error,":[100],"but":[101],"select":[103],"typical":[104],"and":[105,126,171],"atypical":[106],"samples":[108,115,121,129],"from":[109],"train":[111],"set.":[112],"These":[113],"selected":[114],"are":[116],"used":[117],"generate":[119],"positive":[120],"good":[123],"pseudo":[127],"negative":[128],"poor":[131],"respectively":[133],"training":[135],"classifier":[137],"which":[138],"measures":[139],"input":[145],"samples.":[146],"Experiments":[147],"public":[149],"benchmark":[150],"datasets":[151],"show":[152],"that":[153],"our":[154],"achieves":[156],"better":[157],"results":[158],"incorporating":[160],"error":[167],"outperforms":[172],"wide":[174],"range":[175],"existing":[177],"methods.":[178]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}