{"id":"https://openalex.org/W7214945284","doi":"https://doi.org/10.48550/arxiv.2609.37360","title":"Visual Anomaly Synthesis for Model Selection in Data Scarcity","display_name":"Visual Anomaly Synthesis for Model Selection in Data Scarcity","publication_year":2026,"publication_date":"2026-09-29","ids":{"openalex":"https://openalex.org/W7214945284","doi":"https://doi.org/10.48550/arxiv.2609.37360"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.37360","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37360","pdf_url":"https://arxiv.org/pdf/2609.37360","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.37360","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5147772727","display_name":"Daniel Pr\u00f6ll","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I182212641","display_name":"University of Salzburg","ror":"https://ror.org/05gs8cd61","country_code":"AT","type":"education","lineage":["https://openalex.org/I182212641"]},{"id":"https://openalex.org/I4405276866","display_name":"Digital Factory Vorarlberg GmbH","ror":"https://ror.org/00jz46m67","country_code":"AT","type":"facility","lineage":["https://openalex.org/I132118926","https://openalex.org/I4405276866"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Pr\u00f6ll, Daniel","raw_affiliation_strings":["Digital Factory Vorarlberg GmbH , Dornbirn , Austria","University of Salzburg , Salzburg , Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Factory Vorarlberg GmbH , Dornbirn , Austria","institution_ids":["https://openalex.org/I4405276866"]},{"raw_affiliation_string":"University of Salzburg , Salzburg , Austria","institution_ids":["https://openalex.org/I182212641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040153164","display_name":"Thomas Kraxner","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kraxner, Thomas","raw_affiliation_strings":["illwerke vkw AG , Bregenz , Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"illwerke vkw AG , Bregenz , Austria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064903114","display_name":"Tobias Schaefer","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schaefer, Tobias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063481627","display_name":"Sebastian Hegenbart","orcid":"https://orcid.org/0009-0003-2745-1389","observed_orcids":["https://orcid.org/0009-0003-2745-1389"]},"institutions":[{"id":"https://openalex.org/I4405276866","display_name":"Digital Factory Vorarlberg GmbH","ror":"https://ror.org/00jz46m67","country_code":"AT","type":"facility","lineage":["https://openalex.org/I132118926","https://openalex.org/I4405276866"]},{"id":"https://openalex.org/I675453","display_name":"Vorarlberg University of Applied Sciences","ror":"https://ror.org/031wyx077","country_code":"AT","type":"education","lineage":["https://openalex.org/I675453"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Hegenbart, Sebastian","raw_affiliation_strings":["Digital Factory Vorarlberg GmbH , Dornbirn , Austria","Vorarlberg University of Applied Sciences , Dornbirn , Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Factory Vorarlberg GmbH , Dornbirn , Austria","institution_ids":["https://openalex.org/I4405276866"]},{"raw_affiliation_string":"Vorarlberg University of Applied Sciences , Dornbirn , Austria","institution_ids":["https://openalex.org/I675453"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.8637999892234802,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.8637999892234802,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.07280000299215317,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.024000000208616257,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.8090000152587891},{"id":"https://openalex.org/keywords/data-scarcity","display_name":"data scarcity","score":0.8889999985694885},{"id":"https://openalex.org/keywords/prompt-engineering","display_name":"prompt engineering","score":0.42899999022483826},{"id":"https://openalex.org/keywords/image-editing","display_name":"image editing","score":0.5460000038146973},{"id":"https://openalex.org/keywords/industrial-defect-detection","display_name":"industrial defect detection","score":0.6990000009536743},{"id":"https://openalex.org/keywords/patchcore","display_name":"PatchCore","score":0.8320000171661377}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7039999961853027},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6283000111579895},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.566100001335144},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5321999788284302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5249999761581421},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4196000099182129},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C2778449969","wikidata":"https://www.wikidata.org/wiki/Q130760","display_name":"Turbine","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3571999967098236},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28600001335144043},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28349998593330383},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26080000400543213},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.26019999384880066}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.37360","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37360","pdf_url":"https://arxiv.org/pdf/2609.37360","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2609.37360","doi":"https://doi.org/10.48550/arxiv.2609.37360","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.37360","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.37360","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37360","pdf_url":"https://arxiv.org/pdf/2609.37360","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323031","display_name":"\u00d6sterreichische Forschungsf\u00f6rderungsgesellschaft","ror":"https://ror.org/028jc0449"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214945284.pdf","grobid_xml":"https://content.openalex.org/works/W7214945284.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W4409640729","https://openalex.org/W4414841072","https://openalex.org/W6910554155","https://openalex.org/W7138260181"],"related_works":["https://openalex.org/W7215133732","https://openalex.org/W7117850738","https://openalex.org/W4414518737","https://openalex.org/W7123492821","https://openalex.org/W4410616021","https://openalex.org/W7125358518","https://openalex.org/W7222258095","https://openalex.org/W4417090994","https://openalex.org/W7162875710","https://openalex.org/W4404166214"],"abstract_inverted_index":{"Defect":[0],"detection":[1,117,185,202,207],"systems":[2],"for":[3,22,44,52,60,152,163],"industrial":[4],"condition":[5],"monitoring":[6,165],"can":[7,49,132],"only":[8],"be":[9,50,133],"relied":[10],"upon":[11],"if":[12],"they":[13],"are":[14,19,78,97,108,176],"validated,":[15],"yet":[16],"defective":[17],"samples":[18],"rare":[20,177],"and,":[21],"a":[23,30,87,112],"specific":[24],"asset,":[25,47],"often":[26],"nonexistent.":[27],"We":[28],"present":[29],"framework":[31],"that":[32,48],"synthesizes":[33],"severity-graded":[34,153],"defects":[35,224],"on":[36,122,189],"real":[37,173],"non-defective":[38],"images":[39,83,175,192],"without":[40],"any":[41],"defect":[42,58,73,174],"references":[43],"the":[45,104,138,150,160,228],"target":[46],"used":[51],"model":[53,91,130,141,186,215],"selection":[54,120,131],"and":[55,84,95,114,178,193,195,211,219],"validation.":[56],"A":[57,156,182],"taxonomy":[59],"common":[61],"failure":[62],"modes":[63],"is":[64,187],"distilled":[65],"from":[66,80],"literature":[67],"into":[68],"prescriptive":[69],"prompts":[70],"at":[71,208],"varying":[72],"severities.":[74],"Regions":[75],"of":[76,166],"interest":[77],"cropped":[79],"in":[81,170],"defect-free":[82],"edited":[85],"with":[86,103,143],"pre-trained":[88],"image":[89,125],"generation":[90],"(\"FLUX.2":[92],"[klein]\").":[93],"Color-matching":[94],"blending":[96],"employed":[98],"to":[99,137,145,180],"improve":[100],"structural":[101],"coherence":[102],"original":[105],"image.":[106],"Generations":[107],"filtered":[109],"out":[110],"by":[111,115],"scorer":[113],"estimated":[116],"difficulty.":[118],"Model":[119],"experiments":[121],"MVTecAD":[123],"show":[124,149],"AUROC":[126,212],"choice":[127],"regret":[128],"over":[129],"nearly":[134],"halved":[135],"compared":[136],"best":[139],"fixed":[140],"chosen":[142],"access":[144],"test":[146],"data.":[147],"Experiments":[148],"need":[151],"anomaly":[154,184],"synthesis.":[155],"case":[157],"study":[158],"investigates":[159],"proposed":[161],"method":[162],"in-situ":[164],"Pelton":[167,190],"turbine":[168,191],"runners":[169],"hydropower,":[171],"where":[172],"expensive":[179],"collect.":[181],"PatchCorebased":[183],"fit":[188],"selected":[194],"validated":[196],"using":[197],"synthetic":[198,229],"images,":[199],"showing":[200],"strong":[201],"performance":[203],"(94":[204],"%":[205],"correct":[206],"optimal":[209],"threshold":[210],"0.97).":[213],"The":[214],"reliably":[216],"detects":[217],"moderate":[218],"advanced":[220],"defects,":[221],"while":[222],"early-stage":[223],"remain":[225],"challenging,":[226],"indicating":[227],"data":[230],"meaningfully":[231],"stresses":[232],"detector":[233],"sensitivity.":[234]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-01T00:00:00"}