{"id":"https://openalex.org/W7164845687","doi":"https://doi.org/10.48550/arxiv.2606.14475","title":"Value-order Decomposition for Generalist Anomaly Detection","display_name":"Value-order Decomposition for Generalist Anomaly Detection","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164845687","doi":"https://doi.org/10.48550/arxiv.2606.14475"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14475","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14475","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.14475","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138656789","display_name":"Miaoyun Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Miaoyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138672308","display_name":"Jing Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138668808","display_name":"Miaoni Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Miaoni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5036390522","display_name":"Q. Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9649999737739563,"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":0.9649999737739563,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.0052999998442828655,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.004699999932199717,"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/generalization","display_name":"Generalization","score":0.8748000264167786},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8399999737739563},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.629800021648407},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5960999727249146},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5583999752998352},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5145000219345093},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4788999855518341},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4424999952316284}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.8748000264167786},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8399999737739563},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.629800021648407},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6230000257492065},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5960999727249146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.583299994468689},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5583999752998352},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5145000219345093},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4788999855518341},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3488999903202057},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3456000089645386},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.33379998803138733},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C45371612","wikidata":"https://www.wikidata.org/wiki/Q3058587","display_name":"Generalist and specialist species","level":3,"score":0.27059999108314514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14475","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14475","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,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.14475","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14475","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,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Industrial":[0],"anomaly":[1,174,205],"detection":[2,175],"suffers":[3],"from":[4],"limited":[5],"data,":[6],"making":[7],"cross-domain":[8,73],"generalization":[9,53,116,154,210],"particularly":[10],"challenging.":[11],"Generalist":[12],"Anomaly":[13],"Detection":[14],"(GAD)":[15],"aims":[16],"to":[17,83,92,185],"train":[18],"a":[19,23,65,106,202],"unified":[20],"model":[21],"on":[22,191],"source":[24],"domain":[25],"that":[26,111,198],"can":[27],"effectively":[28,183],"detect":[29],"anomalies":[30,44],"in":[31],"unseen":[32,186],"target":[33],"domains.":[34,55,130],"In":[35],"the":[36,156,160,169,212],"initial":[37],"semantic":[38],"feature":[39],"space,":[40],"strong":[41,161,209],"entanglement":[42],"between":[43,163],"and":[45,88,95,125,128,133,137,144,165,179,182,194],"object":[46,84,119],"categories":[47],"or":[48],"defect":[49,86,121,188],"types":[50,87,114,122],"hinders":[51],"effective":[52,109],"across":[54,118,155,211],"Recent":[56],"works":[57],"address":[58,98],"this":[59,99],"issue":[60],"by":[61],"projecting":[62],"features":[63,80],"into":[64],"residual":[66],"space;":[67],"however,":[68],"such":[69],"methods":[70],"primarily":[71],"increase":[72],"overlap":[74],"for":[75],"normal":[76,143,178],"features,":[77],"while":[78,147],"anomalous":[79],"remain":[81],"specific":[82],"categories,":[85,120],"data":[89,129],"domains,":[90],"leading":[91],"poor":[93],"alignment":[94,141,162],"generalization.":[96],"To":[97],"limitation,":[100],"we":[101,172],"propose":[102],"Value-order":[103],"Decomposition":[104],"(VOD),":[105],"simple":[107,203],"yet":[108],"technique":[110],"bridges":[112],"\\textbf{three":[113],"of":[115],"gaps}":[117],"(including":[123],"real":[124,164,187],"synthetic":[126,166],"defects),":[127],"VOD":[131],"disentangles":[132],"suppresses":[134],"object-category-,":[135],"defect-type-,":[136],"domain-specific":[138],"information,":[139],"promoting":[140],"within":[142,168],"abnormal":[145],"samples":[146],"preserving":[148],"their":[149],"separability,":[150],"thereby":[151],"enabling":[152],"robust":[153],"three":[157,213],"gaps.":[158,214],"Leveraging":[159],"defects":[167],"same":[170],"object,":[171],"perform":[173],"using":[176,201],"only":[177],"synthetic-abnormal":[180],"reference,":[181],"generalize":[184],"types.":[189],"Experiments":[190],"diverse":[192],"industrial":[193],"medical":[195],"benchmarks":[196],"demonstrate":[197],"our":[199],"method,":[200],"cut-and-paste":[204],"simulation":[206],"strategy,":[207],"achieves":[208]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-16T00:00:00"}