{"id":"https://openalex.org/W4304172574","doi":"https://doi.org/10.3390/s22197674","title":"Fast and Efficient Image Novelty Detection Based on Mean-Shifts","display_name":"Fast and Efficient Image Novelty Detection Based on Mean-Shifts","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304172574","doi":"https://doi.org/10.3390/s22197674","pmid":"https://pubmed.ncbi.nlm.nih.gov/36236774"},"language":"en","primary_location":{"id":"doi:10.3390/s22197674","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197674","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7674/pdf?version=1665395073","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/19/7674/pdf?version=1665395073","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088939618","display_name":"Matthias Hermann","orcid":"https://orcid.org/0000-0002-0694-7719"},"institutions":[{"id":"https://openalex.org/I9649716","display_name":"HTWG Hochschule Konstanz - Technik, Wirtschaft und Gestaltung","ror":"https://ror.org/051qw9f78","country_code":"DE","type":"education","lineage":["https://openalex.org/I9649716"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Matthias Hermann","raw_affiliation_strings":["Institute for Optical Systems, HTWG Konstanz\u2014University of Applied Sciences, Alfred-Wachtel-Stra\u00dfe 8, 78462 Konstanz, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Optical Systems, HTWG Konstanz\u2014University of Applied Sciences, Alfred-Wachtel-Stra\u00dfe 8, 78462 Konstanz, Germany","institution_ids":["https://openalex.org/I9649716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080691831","display_name":"Georg Umlauf","orcid":"https://orcid.org/0000-0002-7675-8059"},"institutions":[{"id":"https://openalex.org/I9649716","display_name":"HTWG Hochschule Konstanz - Technik, Wirtschaft und Gestaltung","ror":"https://ror.org/051qw9f78","country_code":"DE","type":"education","lineage":["https://openalex.org/I9649716"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Georg Umlauf","raw_affiliation_strings":["Institute for Optical Systems, HTWG Konstanz\u2014University of Applied Sciences, Alfred-Wachtel-Stra\u00dfe 8, 78462 Konstanz, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Optical Systems, HTWG Konstanz\u2014University of Applied Sciences, Alfred-Wachtel-Stra\u00dfe 8, 78462 Konstanz, Germany","institution_ids":["https://openalex.org/I9649716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111966129","display_name":"Bastian Goldl\u00fccke","orcid":null},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bastian Goldl\u00fccke","raw_affiliation_strings":["Department of Computer Science, University of Konstanz, Universit\u00e4tsstra\u00dfe 10, 78464 Konstanz, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Konstanz, Universit\u00e4tsstra\u00dfe 10, 78464 Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012844162","display_name":"Matthias Franz","orcid":"https://orcid.org/0000-0003-3789-8849"},"institutions":[{"id":"https://openalex.org/I9649716","display_name":"HTWG Hochschule Konstanz - Technik, Wirtschaft und Gestaltung","ror":"https://ror.org/051qw9f78","country_code":"DE","type":"education","lineage":["https://openalex.org/I9649716"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Matthias O. Franz","raw_affiliation_strings":["Institute for Optical Systems, HTWG Konstanz\u2014University of Applied Sciences, Alfred-Wachtel-Stra\u00dfe 8, 78462 Konstanz, Germany"],"raw_orcid":"https://orcid.org/0000-0003-3789-8849","affiliations":[{"raw_affiliation_string":"Institute for Optical Systems, HTWG Konstanz\u2014University of Applied Sciences, Alfred-Wachtel-Stra\u00dfe 8, 78462 Konstanz, Germany","institution_ids":["https://openalex.org/I9649716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5088939618"],"corresponding_institution_ids":["https://openalex.org/I9649716"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":0.6944,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.75553351,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"22","issue":"19","first_page":"7674","last_page":"7674"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9659000039100647,"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/T11684","display_name":"Vibrio bacteria research studies","score":0.9549999833106995,"subfield":{"id":"https://openalex.org/subfields/1310","display_name":"Endocrinology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7589619159698486},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7223235368728638},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6535610556602478},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6451568007469177},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5602093935012817},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5504733324050903},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.526548445224762},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4672439396381378},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4529484808444977},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44486573338508606},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44155943393707275}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7589619159698486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7223235368728638},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6535610556602478},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6451568007469177},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5602093935012817},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5504733324050903},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.526548445224762},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4672439396381378},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4529484808444977},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44486573338508606},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44155943393707275}],"mesh":[{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":7,"locations":[{"id":"doi:10.3390/s22197674","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197674","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7674/pdf?version=1665395073","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36236774","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36236774","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:kops.uni-konstanz.de:123456789/58928","is_oa":true,"landing_page_url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1q5sij66uivsn1","pdf_url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/3bc7ca72-5584-4280-bf27-c533d39dac06/content","source":{"id":"https://openalex.org/S4306401487","display_name":"KOPS (University of Konstanz)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189712700","host_organization_name":"University of Konstanz","host_organization_lineage":["https://openalex.org/I189712700"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors. MDPI. 2022, 22(19), 7674. eISSN 1424-8220. Available under: doi: 10.3390/s22197674","raw_type":"doc-type:Text"},{"id":"pmh:oai:doaj.org/article:152a0941f61b4b3e9c938eb9c0bf6e67","is_oa":true,"landing_page_url":"https://doaj.org/article/152a0941f61b4b3e9c938eb9c0bf6e67","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 19, p 7674 (2022)","raw_type":"article"},{"id":"pmh:oai:elib.uni-konstanz.de-htwg:3279","is_oa":true,"landing_page_url":"https://opus.htwg-konstanz.de/frontdoor/index/index/docId/3279","pdf_url":null,"source":{"id":"https://openalex.org/S4306400170","display_name":"URN-Resolver at the German National Library (German National Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2802635041","host_organization_name":"Technische Informationsbibliothek (TIB)","host_organization_lineage":["https://openalex.org/I2802635041"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doc-type:article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/19/7674/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22197674","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 19; Pages: 7674","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9573479","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9573479","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22197674","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197674","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7674/pdf?version=1665395073","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G404480018","display_name":null,"funder_award_id":"01IS19083A","funder_id":"https://openalex.org/F4320321114","funder_display_name":"Bundesministerium f\u00fcr Bildung und Forschung"}],"funders":[{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4304172574.pdf","grobid_xml":"https://content.openalex.org/works/W4304172574.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W2038869416","https://openalex.org/W2064771454","https://openalex.org/W2099741732","https://openalex.org/W2105497548","https://openalex.org/W2115627867","https://openalex.org/W2117539524","https://openalex.org/W2405787682","https://openalex.org/W2556967412","https://openalex.org/W2803674491","https://openalex.org/W2804636549","https://openalex.org/W2807955733","https://openalex.org/W2944378183","https://openalex.org/W2946948417","https://openalex.org/W2948982773","https://openalex.org/W2964137095","https://openalex.org/W2986661129","https://openalex.org/W3038008725","https://openalex.org/W3089028909","https://openalex.org/W3108975329","https://openalex.org/W3118895125","https://openalex.org/W3124158341","https://openalex.org/W3129166376","https://openalex.org/W3147184966","https://openalex.org/W3153872861","https://openalex.org/W3159879667","https://openalex.org/W3160366495","https://openalex.org/W3166166117","https://openalex.org/W3169651898","https://openalex.org/W3172977855","https://openalex.org/W3180957062","https://openalex.org/W4285201299","https://openalex.org/W6751494907","https://openalex.org/W6762744983","https://openalex.org/W6774611950","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2140186469","https://openalex.org/W2626256601","https://openalex.org/W4390421286","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W3107369729","https://openalex.org/W2160692033"],"abstract_inverted_index":{"Image":[0,158],"novelty":[1],"detection":[2,13],"is":[3,110],"a":[4,19,84,90,121,194,214],"repeating":[5],"task":[6],"in":[7,33,193],"computer":[8],"vision":[9],"and":[10,48,64,75,145,179,188,218,221],"describes":[11],"the":[12,40,46,57,72,100,106,115,130,134,146,168,184,200,205],"of":[14,24,53,59,114,133,171,204],"anomalous":[15],"images":[16,50],"based":[17],"on":[18,183],"training":[20,47],"dataset":[21,186],"consisting":[22],"solely":[23],"normal":[25,62],"reference":[26],"data.":[27],"It":[28],"has":[29],"been":[30],"found":[31],"that,":[32],"particular,":[34],"neural":[35,92],"networks":[36],"are":[37,68,78],"well-suited":[38],"for":[39],"task.":[41],"Our":[42],"approach":[43],"first":[44],"transforms":[45],"test":[49,109],"into":[51],"ensembles":[52],"patches,":[54],"which":[55],"enables":[56],"assessment":[58],"mean-shifts":[60,67],"between":[61],"data":[63],"outliers.":[65],"As":[66],"only":[69],"detectable":[70],"when":[71],"outlier":[73],"ensemble":[74],"inlier":[76],"distribution":[77],"spatially":[79],"separate":[80],"from":[81],"each":[82],"other,":[83],"rich":[85],"feature":[86,148],"space,":[87,149],"such":[88],"as":[89,150],"pre-trained":[91],"network,":[93],"needs":[94,125],"to":[95,98,119],"be":[96,120,211],"chosen":[97,147],"represent":[99],"extracted":[101],"patches.":[102],"For":[103],"mean-shift":[104],"estimation,":[105],"Hotelling":[107],"T2":[108],"used.":[111],"The":[112],"size":[113,132],"patches":[116],"turned":[117],"out":[118],"crucial":[122],"hyperparameter":[123],"that":[124],"additional":[126,216],"domain":[127],"knowledge":[128],"about":[129],"spatial":[131],"expected":[135],"anomalies":[136],"(local":[137],"vs.":[138],"global).":[139],"This":[140],"also":[141],"affects":[142],"model":[143],"selection":[144],"commonly":[151],"used":[152],"Convolutional":[153],"Neural":[154],"Networks":[155],"or":[156],"Vision":[157],"Transformers":[159],"have":[160],"very":[161,229],"different":[162],"receptive":[163],"field":[164],"sizes.":[165],"To":[166],"showcase":[167],"state-of-the-art":[169],"capabilities":[170],"our":[172,208],"approach,":[173],"we":[174],"compare":[175],"results":[176],"with":[177],"classical":[178],"deep":[180],"learning":[181],"methods":[182],"popular":[185],"CIFAR-10,":[187],"demonstrate":[189],"its":[190],"real-world":[191],"applicability":[192],"large-scale":[195],"industrial":[196],"inspection":[197],"scenario":[198],"using":[199],"MVTec":[201],"dataset.":[202],"Because":[203],"inexpensive":[206],"design,":[207],"method":[209],"can":[210],"implemented":[212],"by":[213],"single":[215],"2D-convolution":[217],"pooling":[219],"layer":[220],"allows":[222],"particularly":[223],"fast":[224],"prediction":[225],"times":[226],"while":[227],"being":[228],"data-efficient.":[230]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}