{"id":"https://openalex.org/W4393853540","doi":"https://doi.org/10.1117/12.3023369","title":"Incremental one-class learning using regularized null-space training for industrial defect detection","display_name":"Incremental one-class learning using regularized null-space training for industrial defect detection","publication_year":2024,"publication_date":"2024-04-03","ids":{"openalex":"https://openalex.org/W4393853540","doi":"https://doi.org/10.1117/12.3023369"},"language":"en","primary_location":{"id":"doi:10.1117/12.3023369","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.3023369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sixteenth International Conference on Machine Vision (ICMV 2023)","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/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":["Hochschule Konstanz (Germany)"],"affiliations":[{"raw_affiliation_string":"Hochschule 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":["Hochschule Konstanz (Germany)"],"affiliations":[{"raw_affiliation_string":"Hochschule 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":["Universit\u00e4t Konstanz (Germany)"],"affiliations":[{"raw_affiliation_string":"Universit\u00e4t 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":["Hochschule Konstanz (Germany)"],"affiliations":[{"raw_affiliation_string":"Hochschule 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":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03330193,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"24","issue":null,"first_page":"19","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994000196456909,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994000196456909,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9909999966621399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8217238187789917},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.666564404964447},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6331264972686768},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6246955990791321},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5974243879318237},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5545932054519653},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.49775364995002747},{"id":"https://openalex.org/keywords/null-hypothesis","display_name":"Null hypothesis","score":0.4577223062515259},{"id":"https://openalex.org/keywords/null","display_name":"Null (SQL)","score":0.4232605993747711},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3263664245605469},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3220650553703308},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2376651167869568},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23133787512779236},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09963464736938477}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8217238187789917},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.666564404964447},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6331264972686768},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6246955990791321},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5974243879318237},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5545932054519653},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.49775364995002747},{"id":"https://openalex.org/C191988596","wikidata":"https://www.wikidata.org/wiki/Q628374","display_name":"Null hypothesis","level":2,"score":0.4577223062515259},{"id":"https://openalex.org/C203763787","wikidata":"https://www.wikidata.org/wiki/Q371029","display_name":"Null (SQL)","level":2,"score":0.4232605993747711},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3263664245605469},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3220650553703308},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2376651167869568},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23133787512779236},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09963464736938477},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1117/12.3023369","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.3023369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sixteenth International Conference on Machine Vision (ICMV 2023)","raw_type":"proceedings-article"},{"id":"pmh:oai:elib.uni-konstanz.de-htwg:5148","is_oa":false,"landing_page_url":"https://opus.htwg-konstanz.de/frontdoor/index/index/docId/5148","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doc-type:conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1522301498","https://openalex.org/W1682403713","https://openalex.org/W1964694549","https://openalex.org/W2037979274","https://openalex.org/W2068434339","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2601450892","https://openalex.org/W2767106145","https://openalex.org/W2953989358","https://openalex.org/W2963741406","https://openalex.org/W3034942609","https://openalex.org/W3094502228","https://openalex.org/W3097784654","https://openalex.org/W3163842339","https://openalex.org/W3163939464","https://openalex.org/W4242177601","https://openalex.org/W4299518610","https://openalex.org/W4312277011","https://openalex.org/W4380322329","https://openalex.org/W6631190155","https://openalex.org/W6637298292","https://openalex.org/W6682132143","https://openalex.org/W6684191040","https://openalex.org/W6686164453","https://openalex.org/W6735236233","https://openalex.org/W6750389746","https://openalex.org/W6753554323","https://openalex.org/W6758126075","https://openalex.org/W6762718338","https://openalex.org/W6763964532","https://openalex.org/W6767101766","https://openalex.org/W6776331362","https://openalex.org/W6790937858","https://openalex.org/W6791536121","https://openalex.org/W6810240096","https://openalex.org/W6977528248"],"related_works":["https://openalex.org/W4306992803","https://openalex.org/W2979681497","https://openalex.org/W4306992794","https://openalex.org/W2590542766","https://openalex.org/W4306992806","https://openalex.org/W4239137652","https://openalex.org/W4306992821","https://openalex.org/W1999826675","https://openalex.org/W2130815278","https://openalex.org/W2369782938"],"abstract_inverted_index":{"One-class":[0],"incremental":[1,174],"learning":[2],"is":[3,16,29,62,188,209],"a":[4,12,55,196],"special":[5],"case":[6,28,171],"of":[7,24,97,153,172,204],"class-incremental":[8,248],"learning,":[9],"where":[10,36],"only":[11,54],"single":[13],"novel":[14,37,57],"class":[15],"incrementally":[17,49],"added":[18,210],"to":[19,74,149,158,194,211,244],"an":[20],"existing":[21,115,159],"classifier":[22,68,142],"instead":[23],"multiple":[25],"classes.":[26,160],"This":[27],"relevant":[30],"in":[31,50,169,185,220],"industrial":[32,234],"defect":[33],"detection":[34],"scenarios,":[35],"defects":[38],"usually":[39],"appear":[40],"during":[41],"operation.":[42],"Existing":[43],"rolled-out":[44],"classifiers":[45],"must":[46,69],"be":[47,71],"updated":[48],"this":[51,165],"scenario":[52,223],"with":[53,94,156],"few":[56],"examples.":[58],"In":[59,132,176],"addition,":[60],"it":[61,92],"often":[63,82],"required":[64],"that":[65,181,200],"the":[66,84,95,102,122,140,150,154,170,189,202,212,221,229,242],"base":[67,103],"not":[70],"altered":[72],"due":[73],"approval":[75],"and":[76,89,117,216,238,240],"warranty":[77],"restrictions.":[78],"While":[79],"simple":[80],"finetuning":[81],"gives":[83],"best":[85],"performance":[86,100,243],"across":[87],"old":[88],"new":[90],"classes,":[91],"comes":[93],"drawback":[96],"potentially":[98],"losing":[99],"on":[101,232],"classes":[104,123],"(catastrophic":[105],"forgetting":[106],"[1]).":[107],"Simple":[108],"prototype":[109],"approaches":[110],"[2]":[111],"work":[112],"without":[113],"changing":[114],"weights":[116],"perform":[118],"very":[119],"well":[120,125],"when":[121,130],"are":[124,147],"separated":[126],"but":[127],"fail":[128],"dramatically":[129],"not.":[131],"theory,":[133],"null-space":[134,218],"training":[135,219],"(NSCL)":[136],"[3]":[137],"should":[138],"retain":[139],"basis":[141],"entirely,":[143],"as":[144,162],"parameter":[145],"updates":[146],"restricted":[148],"null":[151,186],"space":[152,187],"network":[155],"respect":[157],"However,":[161],"we":[163,179],"show,":[164],"technique":[166],"promotes":[167],"overfitting":[168],"one-class":[173,222],"learning.":[175,249],"our":[177],"experiments,":[178],"found":[180],"unconstrained":[182],"weight":[183],"growth":[184],"underlying":[190],"issue,":[191],"leading":[192],"us":[193],"propose":[195],"regularization":[197,207],"term":[198,208],"(R-NSCL)":[199],"penalizes":[201],"magnitude":[203],"amplification.":[205],"The":[206],"standard":[213],"classification":[214],"loss":[215],"stabilizes":[217],"by":[224],"counteracting":[225],"overfitting.":[226],"We":[227],"test":[228],"method\u2019s":[230],"capabilities":[231],"two":[233],"datasets,":[235],"namely":[236],"AITEX":[237],"MVTec,":[239],"compare":[241],"state-of-the-art":[245],"algorithms":[246],"for":[247]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
