{"id":"https://openalex.org/W4386598388","doi":"https://doi.org/10.1109/icip49359.2023.10222183","title":"Patch-Wise Auto-Encoder for Visual Anomaly Detection","display_name":"Patch-Wise Auto-Encoder for Visual Anomaly Detection","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386598388","doi":"https://doi.org/10.1109/icip49359.2023.10222183"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5023617465","display_name":"Yajie Cui","orcid":"https://orcid.org/0000-0003-2958-898X"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yajie Cui","raw_affiliation_strings":["China Unicom,Unicom Digital Technology,Beijing,China,100013"],"affiliations":[{"raw_affiliation_string":"China Unicom,Unicom Digital Technology,Beijing,China,100013","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101742159","display_name":"Zhaoxiang Liu","orcid":"https://orcid.org/0000-0002-1267-0277"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoxiang Liu","raw_affiliation_strings":["China Unicom,Unicom Digital Technology,Beijing,China,100013"],"affiliations":[{"raw_affiliation_string":"China Unicom,Unicom Digital Technology,Beijing,China,100013","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066958531","display_name":"Shiguo Lian","orcid":"https://orcid.org/0000-0003-4308-7049"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiguo Lian","raw_affiliation_strings":["China Unicom,Unicom Digital Technology,Beijing,China,100013"],"affiliations":[{"raw_affiliation_string":"China Unicom,Unicom Digital Technology,Beijing,China,100013","institution_ids":["https://openalex.org/I6507939"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023617465"],"corresponding_institution_ids":["https://openalex.org/I6507939"],"apc_list":null,"apc_paid":null,"fwci":0.3503,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65513303,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"870","last_page":"874"},"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":0.9817000031471252,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9631999731063843,"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":0.7710567116737366},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6666582822799683},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6563223600387573},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6477760076522827},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6362259387969971},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6290162801742554},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5993188619613647},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5563680529594421},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5151306390762329},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5141449570655823},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.46911078691482544},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.45162433385849},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.41995829343795776},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41439563035964966},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4106053411960602},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.16240659356117249},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11814340949058533},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.11686012148857117}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7710567116737366},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6666582822799683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6563223600387573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6477760076522827},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6362259387969971},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6290162801742554},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5993188619613647},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5563680529594421},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5151306390762329},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5141449570655823},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.46911078691482544},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.45162433385849},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.41995829343795776},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41439563035964966},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4106053411960602},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.16240659356117249},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11814340949058533},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.11686012148857117},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10222183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"},{"score":0.4399999976158142,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W2791709739","https://openalex.org/W2948982773","https://openalex.org/W2987228832","https://openalex.org/W3034916114","https://openalex.org/W3035240825","https://openalex.org/W3092704883","https://openalex.org/W3106848223","https://openalex.org/W3118895125","https://openalex.org/W3147184966","https://openalex.org/W3159648608","https://openalex.org/W3166166117","https://openalex.org/W3169651898","https://openalex.org/W3183588514","https://openalex.org/W3204520143","https://openalex.org/W3212044949","https://openalex.org/W4287887190","https://openalex.org/W4313156423","https://openalex.org/W4319299981","https://openalex.org/W4376626035","https://openalex.org/W6610566761","https://openalex.org/W6640963894","https://openalex.org/W6771530079"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1],"without":[2],"priors":[3],"of":[4,12,63,68,73,83,95,116],"the":[5,10,24,33,45,60,84,105,114],"anomalies":[6,66],"is":[7,75,99],"challenging.":[8],"In":[9],"field":[11],"unsupervised":[13],"anomaly":[14],"detection,":[15],"traditional":[16],"auto-encoder":[17,52],"(AE)":[18],"tends":[19],"to":[20,39,65],"fail":[21],"based":[22],"on":[23,30,108],"assumption":[25],"that":[26],"by":[27,77],"training":[28],"only":[29],"normal":[31],"images,":[32],"model":[34],"will":[35],"not":[36],"be":[37],"able":[38],"reconstruct":[40],"abnormal":[41],"images":[42],"correctly.":[43],"On":[44],"contrary,":[46],"we":[47],"propose":[48],"a":[49],"novel":[50],"patch-wise":[51,89],"(Patch":[53],"AE)":[54],"framework,":[55],"which":[56,91,112],"aims":[57],"at":[58],"enhancing":[59],"reconstruction":[61],"ability":[62],"AE":[64],"instead":[67],"weakening":[69],"it.":[70],"Each":[71],"patch":[72],"image":[74],"reconstructed":[76],"corresponding":[78],"spatially":[79],"distributed":[80],"feature":[81,86],"vector":[82],"learned":[85],"representation,":[87],"i.e.,":[88],"reconstruction,":[90],"ensures":[92],"anomaly-":[93],"sensitivity":[94],"AE.":[96],"Our":[97],"method":[98],"simple":[100],"and":[101],"efficient.":[102],"It":[103,119],"advances":[104],"state-of-the-art":[106],"performances":[107],"Mvtec":[109],"AD":[110],"benchmark,":[111],"proves":[113],"effectiveness":[115],"our":[117],"model.":[118],"shows":[120],"great":[121],"potential":[122],"in":[123],"practical":[124],"industrial":[125],"application":[126],"scenarios.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}