{"id":"https://openalex.org/W4295308550","doi":"https://doi.org/10.1109/tim.2022.3205674","title":"Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection","display_name":"Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4295308550","doi":"https://doi.org/10.1109/tim.2022.3205674"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2022.3205674","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3205674","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5033335952","display_name":"Qian Wan","orcid":"https://orcid.org/0000-0002-9517-7698"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Wan","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-9517-7698","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017886286","display_name":"Liang Gao","orcid":"https://orcid.org/0000-0002-1485-0722"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Gao","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-1485-0722","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100406104","display_name":"Xinyu Li","orcid":"https://orcid.org/0000-0002-3730-0360"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Li","raw_affiliation_strings":["State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-3730-0360","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":2.7555,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.9158733,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"71","issue":null,"first_page":"1","last_page":"12"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9969000220298767,"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"}},{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9955999851226807,"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/anomaly-detection","display_name":"Anomaly detection","score":0.8009831309318542},{"id":"https://openalex.org/keywords/abnormality","display_name":"Abnormality","score":0.7999240159988403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7428877353668213},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7034444212913513},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.681778073310852},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5365580916404724},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44717147946357727},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.42722123861312866},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35441017150878906}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8009831309318542},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.7999240159988403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7428877353668213},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7034444212913513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.681778073310852},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5365580916404724},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44717147946357727},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.42722123861312866},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35441017150878906},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/tim.2022.3205674","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3205674","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G234177690","display_name":null,"funder_award_id":"52188102","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4443669366","display_name":null,"funder_award_id":"U21B2029","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1970520480","https://openalex.org/W2194775991","https://openalex.org/W2788633781","https://openalex.org/W2825063406","https://openalex.org/W2905065306","https://openalex.org/W2920946673","https://openalex.org/W2944303778","https://openalex.org/W2963351448","https://openalex.org/W2963691377","https://openalex.org/W2998291476","https://openalex.org/W3034314048","https://openalex.org/W3082184285","https://openalex.org/W3089028909","https://openalex.org/W3096121526","https://openalex.org/W3106848223","https://openalex.org/W3109715690","https://openalex.org/W3109771882","https://openalex.org/W3129166376","https://openalex.org/W3153381206","https://openalex.org/W3166596953","https://openalex.org/W3168175245","https://openalex.org/W3169077988","https://openalex.org/W3171079288","https://openalex.org/W3177716034","https://openalex.org/W3183588514","https://openalex.org/W3194400333","https://openalex.org/W3201753566","https://openalex.org/W3202232857","https://openalex.org/W3206816884","https://openalex.org/W3207382730","https://openalex.org/W4212774754","https://openalex.org/W4213436958","https://openalex.org/W4214694907","https://openalex.org/W4224073290","https://openalex.org/W4224999518","https://openalex.org/W4285147180","https://openalex.org/W4312772600","https://openalex.org/W6637373629","https://openalex.org/W6751494907","https://openalex.org/W6763324549","https://openalex.org/W6764733053","https://openalex.org/W6780793664","https://openalex.org/W6797053244","https://openalex.org/W6802573413","https://openalex.org/W6802739845"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2143820878","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969"],"abstract_inverted_index":{"Image":[0],"Anomaly":[1],"Detection":[2],"is":[3,93,106,123,165],"a":[4,72,85,102,110,142],"significant":[5],"stage":[6],"for":[7,35,48,112,135,193],"visual":[8],"quality":[9],"inspection":[10],"in":[11,50],"intelligent":[12],"manufacturing":[13],"systems.":[14],"According":[15],"to":[16,64,71,95,108,125],"the":[17,26,56,66,137,146,151,157,161,169],"assumption":[18],"that":[19],"only":[20,131],"normal":[21],"images":[22,41,134,192],"are":[23],"available":[24],"during":[25],"training":[27,49],"stage,":[28],"unsupervised":[29,57],"methods":[30,58,171],"have":[31],"been":[32],"studied":[33],"recently":[34],"image":[36,74,98,147],"anomaly":[37,75,99,127,148],"detection.":[38,100,116,128],"But":[39],"anomalous":[40,133,191],"of":[42,62,150,183],"small":[43],"scale":[44],"can":[45],"be":[46],"collected":[47],"many":[51],"real-world":[52],"industrial":[53],"scenarios,":[54],"and":[55,175,186],"make":[59],"no":[60],"use":[61],"them":[63],"improve":[65],"detection":[67,76,80,149],"accuracy.":[68],"This":[69],"leads":[70],"semi-supervised":[73,97],"with":[77,88,114,156,168,188],"an":[78,119],"unbalanced":[79,115],"challenge.":[81],"In":[82],"this":[83],"paper,":[84],"Logit":[86,103],"Inducing":[87,104],"Abnormality":[89,120],"Capturing":[90,121],"(LIAC)":[91],"method":[92,140,164],"proposed":[94,107,124,138,162],"address":[96,126],"Firstly,":[101],"Loss":[105],"train":[109],"classifier":[111],"dealing":[113],"And":[117],"secondly,":[118],"Module":[122],"With":[129],"labeling":[130],"40":[132,190],"training,":[136],"LIAC":[139,163],"achieves":[141,181],"98.8%":[143],"f1-score":[144,182],"on":[145,172],"printed":[152],"circuit":[153],"board,":[154],"compared":[155,167],"state-of-the-art":[158,170],"methods.":[159],"More,":[160],"experimentally":[166],"MTD,":[173],"ROCT,":[174],"ELPV":[176],"three":[177],"open-source":[178],"datasets,":[179],"respectively":[180],"85.2%,":[184],"96.8%,":[185],"66.6%":[187],"given":[189],"training.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
