{"id":"https://openalex.org/W7140776735","doi":"https://doi.org/10.48550/arxiv.2603.23677","title":"Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection","display_name":"Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140776735","doi":"https://doi.org/10.48550/arxiv.2603.23677"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23677","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.23677","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gul, Shreen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gul, Shreen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052668861","display_name":"Mohamed Elmahallawy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elmahallawy, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056760458","display_name":"Ardhendu Tripathy","orcid":"https://orcid.org/0000-0003-1893-4891"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tripathy, Ardhendu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130641855","display_name":"Sanjay Madria","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madria, Sanjay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.5389000177383423,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.5389000177383423,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.37929999828338623,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.02459999918937683,"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/discriminative-model","display_name":"Discriminative model","score":0.7906000018119812},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6729999780654907},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5583000183105469},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5293999910354614},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.49619999527931213},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4586000144481659},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44859999418258667},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43299999833106995}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7906000018119812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6898999810218811},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6729999780654907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6065000295639038},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5583000183105469},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5293999910354614},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.49619999527931213},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4586000144481659},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.39649999141693115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3944000005722046},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.38670000433921814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23677","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.23677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23677","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7167038917541504}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1],"models":[2],"are":[3],"increasingly":[4],"deployed":[5],"in":[6],"safety-critical":[7],"applications,":[8],"where":[9],"reliable":[10],"out-of-distribution":[11],"(OOD)":[12],"detection":[13],"is":[14,190],"essential":[15],"to":[16,46,95,122,162],"ensure":[17],"robustness.":[18],"Existing":[19],"methods":[20],"predominantly":[21],"rely":[22],"on":[23,61,135],"the":[24,33,183],"penultimate-layer":[25],"activations":[26],"of":[27,185],"neural":[28],"networks,":[29],"assuming":[30],"they":[31],"encapsulate":[32],"most":[34],"informative":[35],"in-distribution":[36],"(ID)":[37],"representations.":[38],"In":[39],"this":[40,44,62],"work,":[41],"we":[42,64],"revisit":[43],"assumption":[45],"show":[47],"that":[48,72,143],"intermediate":[49],"layers":[50],"encode":[51],"equally":[52],"rich":[53],"and":[54,91,110,150,164],"discriminative":[55],"information":[56],"for":[57,153,179],"OOD":[58,116,128,137,180],"detection.":[59],"Based":[60],"observation,":[63],"propose":[65],"a":[66,174],"simple":[67],"yet":[68,176],"effective":[69],"model-agnostic":[70],"approach":[71,145],"leverages":[73],"internal":[74],"representations":[75],"across":[76,139],"multiple":[77],"layers.":[78],"Our":[79,188],"scheme":[80],"aggregates":[81],"features":[82,109],"from":[83],"successive":[84],"convolutional":[85],"blocks,":[86],"computes":[87],"class-wise":[88],"mean":[89],"embeddings,":[90],"applies":[92],"L_2":[93],"normalization":[94],"form":[96],"compact":[97],"ID":[98],"prototypes":[99,112],"capturing":[100],"class":[101],"semantics.":[102],"During":[103],"inference,":[104],"cosine":[105],"similarity":[106],"between":[107],"test":[108],"these":[111],"serves":[113],"as":[114,173],"an":[115],"score--ID":[117],"samples":[118,129],"exhibit":[119],"strong":[120,151],"affinity":[121],"at":[123],"least":[124],"one":[125],"prototype,":[126],"whereas":[127],"remain":[130],"uniformly":[131],"distant.":[132],"Extensive":[133],"experiments":[134],"state-of-the-art":[136],"benchmarks":[138],"diverse":[140],"architectures":[141],"demonstrate":[142],"our":[144],"delivers":[146],"robust,":[147],"architecture-agnostic":[148],"performance":[149],"generalization":[152],"image":[154],"classification.":[155],"Notably,":[156],"it":[157],"improves":[158],"AUROC":[159],"by":[160,167],"up":[161],"4.41%":[163],"reduces":[165],"FPR":[166],"13.58%,":[168],"highlighting":[169],"multi-layer":[170],"feature":[171],"aggregation":[172],"powerful":[175],"underexplored":[177],"signal":[178],"detection,":[181],"challenging":[182],"dominance":[184],"penultimate-layer-based":[186],"methods.":[187],"code":[189],"available":[191],"at:":[192],"https://github.com/sgchr273/cosine-layers.git.":[193]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-27T00:00:00"}