{"id":"https://openalex.org/W4309342405","doi":"https://doi.org/10.1109/icumt57764.2022.9943348","title":"Evaluation of Nested U-Net models performance on MVTec AD dataset","display_name":"Evaluation of Nested U-Net models performance on MVTec AD dataset","publication_year":2022,"publication_date":"2022-10-11","ids":{"openalex":"https://openalex.org/W4309342405","doi":"https://doi.org/10.1109/icumt57764.2022.9943348"},"language":"en","primary_location":{"id":"doi:10.1109/icumt57764.2022.9943348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icumt57764.2022.9943348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)","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/A5045643911","display_name":"Martin Jon\u00e1k","orcid":"https://orcid.org/0000-0001-6715-5865"},"institutions":[{"id":"https://openalex.org/I60587646","display_name":"Brno University of Technology","ror":"https://ror.org/03613d656","country_code":"CZ","type":"education","lineage":["https://openalex.org/I60587646"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Martin Jonak","raw_affiliation_strings":["Brno University of Technology,FEEC,Dept. of Telecommunications,Brno,Czech Republic","Dept. of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brno University of Technology,FEEC,Dept. of Telecommunications,Brno,Czech Republic","institution_ids":["https://openalex.org/I60587646"]},{"raw_affiliation_string":"Dept. of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic","institution_ids":["https://openalex.org/I60587646"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104347226","display_name":"\u0160t\u011bp\u00e1n Je\u017eek","orcid":null},"institutions":[{"id":"https://openalex.org/I60587646","display_name":"Brno University of Technology","ror":"https://ror.org/03613d656","country_code":"CZ","type":"education","lineage":["https://openalex.org/I60587646"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Stepan Jezek","raw_affiliation_strings":["Brno University of Technology,FEEC,Dept. of Telecommunications,Brno,Czech Republic","Dept. of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brno University of Technology,FEEC,Dept. of Telecommunications,Brno,Czech Republic","institution_ids":["https://openalex.org/I60587646"]},{"raw_affiliation_string":"Dept. of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic","institution_ids":["https://openalex.org/I60587646"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036042625","display_name":"Radim B\u00fcrget","orcid":"https://orcid.org/0000-0003-1849-5390"},"institutions":[{"id":"https://openalex.org/I60587646","display_name":"Brno University of Technology","ror":"https://ror.org/03613d656","country_code":"CZ","type":"education","lineage":["https://openalex.org/I60587646"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Radim Burget","raw_affiliation_strings":["Brno University of Technology,FEEC,Dept. of Telecommunications,Brno,Czech Republic","Dept. of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brno University of Technology,FEEC,Dept. of Telecommunications,Brno,Czech Republic","institution_ids":["https://openalex.org/I60587646"]},{"raw_affiliation_string":"Dept. of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic","institution_ids":["https://openalex.org/I60587646"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60587646"],"apc_list":null,"apc_paid":null,"fwci":0.1388,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56078966,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"31","issue":null,"first_page":"70","last_page":"75"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9943000078201294,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9901999831199646,"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/computer-science","display_name":"Computer science","score":0.741728663444519},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.636723518371582},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5647125244140625},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5532033443450928},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5083326697349548},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5057132244110107},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5010664463043213},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4806162714958191},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4578491747379303},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4455071687698364},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4381500482559204},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.4221949577331543},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39362356066703796},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11446118354797363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.741728663444519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.636723518371582},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5647125244140625},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5532033443450928},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5083326697349548},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5057132244110107},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5010664463043213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4806162714958191},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4578491747379303},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4455071687698364},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4381500482559204},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.4221949577331543},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39362356066703796},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11446118354797363},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icumt57764.2022.9943348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icumt57764.2022.9943348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5099999904632568}],"awards":[{"id":"https://openalex.org/G2115364973","display_name":null,"funder_award_id":"TA1821S07001,FW03010273","funder_id":"https://openalex.org/F4320315323","funder_display_name":"Technology Agency of the Czech Republic"},{"id":"https://openalex.org/G7247087226","display_name":null,"funder_award_id":"VD182105001,VJ02010019","funder_id":"https://openalex.org/F4320311333","funder_display_name":"Ministerstvo Vnitra \u010cesk\u00e9 Republiky"}],"funders":[{"id":"https://openalex.org/F4320311333","display_name":"Ministerstvo Vnitra \u010cesk\u00e9 Republiky","ror":"https://ror.org/05w1nn565"},{"id":"https://openalex.org/F4320315323","display_name":"Technology Agency of the Czech Republic","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W2007339694","https://openalex.org/W2108598243","https://openalex.org/W2599354622","https://openalex.org/W2774918944","https://openalex.org/W2803446235","https://openalex.org/W2803697594","https://openalex.org/W2809705434","https://openalex.org/W2884436604","https://openalex.org/W2905065306","https://openalex.org/W2911672077","https://openalex.org/W2914570111","https://openalex.org/W2948982773","https://openalex.org/W2963045681","https://openalex.org/W2978971541","https://openalex.org/W3023868590","https://openalex.org/W3024640380","https://openalex.org/W3034986516","https://openalex.org/W3092704883","https://openalex.org/W3101017490","https://openalex.org/W3104156061","https://openalex.org/W3106848223","https://openalex.org/W3114010851","https://openalex.org/W3118608800","https://openalex.org/W3147184966","https://openalex.org/W3159648608","https://openalex.org/W3166166117","https://openalex.org/W3169651898","https://openalex.org/W3183588514","https://openalex.org/W3209793239","https://openalex.org/W3214308028","https://openalex.org/W4281655026","https://openalex.org/W4287887190","https://openalex.org/W4298289240","https://openalex.org/W6637568146","https://openalex.org/W6735108248","https://openalex.org/W6747231328","https://openalex.org/W6751494907","https://openalex.org/W6777869702","https://openalex.org/W6779635041","https://openalex.org/W6779937703","https://openalex.org/W6785596320","https://openalex.org/W6785760050","https://openalex.org/W6795092660","https://openalex.org/W6796767071","https://openalex.org/W6799123661","https://openalex.org/W6803919275"],"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":{"Anomaly":[0],"detection":[1,46,226],"(AD)":[2],"from":[3],"image":[4,173,204,232],"data":[5,159,174],"using":[6],"convolutional":[7],"neural":[8],"networks":[9],"and":[10,21,31,48,98,201,211,229,239],"deep":[11],"learning":[12],"has":[13,61],"become":[14],"a":[15,92,102,112],"widespread":[16],"topic":[17],"among":[18],"both":[19],"scientists":[20],"engineers.":[22],"In":[23],"addition":[24],"to":[25,73,155,169,171],"the":[26,52,86,119,129,139,147,151,187,219],"development":[27],"of":[28,95,104,107,121,131,150,186,196,206,222,234],"new":[29],"methods":[30,55,97],"models,":[32,99],"specialized":[33],"datasets":[34],"are":[35,70],"created":[36,49],"as":[37],"well.":[38],"The":[39,191],"most":[40,53],"cited":[41],"dataset":[42,60],"specialised":[43],"on":[44,85,128,138,224],"anomaly":[45],"tasks":[47],"for":[50,203,231],"testing":[51],"recent":[54],"is":[56,82,101,116,126,144,199,227],"MVTec":[57,87,140],"AD.":[58],"This":[59,124],"been":[62],"used":[63],"in":[64,118],"more":[65],"than":[66],"40":[67],"articles":[68],"which":[69,115],"mainly":[71],"devoted":[72],"creating":[74],"or":[75],"modifying":[76],"AD":[77,88,141],"methods.":[78,190],"Subsequently,":[79],"their":[80,162],"performance":[81,105,195,221],"usually":[83],"tested":[84],"dataset.":[89,142],"However,":[90],"despite":[91],"large":[93],"number":[94],"different":[96],"there":[100],"lack":[103],"evaluation":[106,130],"U-Net++":[108,197],"(Nested":[109],"U-Net":[110,134,153],"architecture),":[111],"robust":[113],"model":[114,198],"well-known":[117],"field":[120],"segmentation":[122],"tasks.":[123],"article":[125],"focused":[127],"two":[132],"Nested":[133,152],"architectures":[135],"(U-Net++,":[136],"ANU-Net)":[137],"It":[143],"shown":[145],"that":[146],"direct":[148],"use":[149],"models":[154],"reconstruct":[156,172],"anomaly-free":[157],"input":[158],"together":[160],"with":[161,175,184],"strong":[163],"augmentation":[164],"during":[165],"training":[166],"phase":[167],"leads":[168],"inability":[170],"anomalies":[176],"at":[177],"inference":[178],"time.":[179],"Achieved":[180],"results":[181],"can":[182],"compete":[183],"some":[185],"state-of-the-art":[188],"reconstruction-based":[189],"average":[192,220],"image-level":[193,225],"AUROC":[194],"97.9%":[200],"96.2%":[202],"size":[205,233],"<tex":[207,212,235,240],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[208,213,236,241],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$64\\times":[209,237],"64$</tex>":[210,238],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$128\\times":[214,242],"128$</tex>":[215,243],"pixels,":[216,244],"respectively.":[217,245],"Further,":[218],"ANU-Net":[223],"96.5%":[228],"96.8%":[230]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}