{"id":"https://openalex.org/W4392903421","doi":"https://doi.org/10.1109/icassp48485.2024.10447053","title":"EPA: Neural Collapse Inspired Robust Out-of-distribution Detector","display_name":"EPA: Neural Collapse Inspired Robust Out-of-distribution Detector","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392903421","doi":"https://doi.org/10.1109/icassp48485.2024.10447053"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100462845","display_name":"Jiawei Zhang","orcid":"https://orcid.org/0000-0002-8634-1687"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Zhang","raw_affiliation_strings":["Tsinghua University,Department of Electronic Engineering,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100628854","display_name":"Yufan Chen","orcid":"https://orcid.org/0000-0003-2064-7541"},"institutions":[{"id":"https://openalex.org/I4210163363","display_name":"PLA Army Engineering University","ror":"https://ror.org/05mgp8x93","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163363"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufan Chen","raw_affiliation_strings":["Army Engineering University of PLA,College of Communication Engineering","College of Communication Engineering, Army Engineering University of PLA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Army Engineering University of PLA,College of Communication Engineering","institution_ids":["https://openalex.org/I4210163363"]},{"raw_affiliation_string":"College of Communication Engineering, Army Engineering University of PLA","institution_ids":["https://openalex.org/I4210163363"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100625873","display_name":"Cheng Jin","orcid":"https://orcid.org/0000-0003-3063-1957"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Jin","raw_affiliation_strings":["Tsinghua University,Department of Electronic Engineering,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020295377","display_name":"Lei Zhu","orcid":"https://orcid.org/0000-0001-7001-5775"},"institutions":[{"id":"https://openalex.org/I4210163363","display_name":"PLA Army Engineering University","ror":"https://ror.org/05mgp8x93","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163363"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhu","raw_affiliation_strings":["Army Engineering University of PLA,College of Communication Engineering","College of Communication Engineering, Army Engineering University of PLA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Army Engineering University of PLA,College of Communication Engineering","institution_ids":["https://openalex.org/I4210163363"]},{"raw_affiliation_string":"College of Communication Engineering, Army Engineering University of PLA","institution_ids":["https://openalex.org/I4210163363"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100621681","display_name":"Yuantao Gu","orcid":"https://orcid.org/0000-0002-8427-1021"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuantao Gu","raw_affiliation_strings":["Tsinghua University,Department of Electronic Engineering,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6515","last_page":"6519"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983999729156494,"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/subspace-topology","display_name":"Subspace topology","score":0.9311378002166748},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.736452579498291},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5560782551765442},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5219570994377136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4991719722747803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4943370819091797},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4879898428916931},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4581647515296936},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4294436275959015},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.42923474311828613},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4126330018043518},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41115960478782654},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3564033508300781},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.24362540245056152}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.9311378002166748},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.736452579498291},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5560782551765442},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5219570994377136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4991719722747803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4943370819091797},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4879898428916931},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4581647515296936},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4294436275959015},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.42923474311828613},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4126330018043518},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41115960478782654},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3564033508300781},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.24362540245056152},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.6499999761581421}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"},{"id":"https://openalex.org/F4320336475","display_name":"National Safety Academic Fund","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2047643928","https://openalex.org/W2797977484","https://openalex.org/W2889625178","https://openalex.org/W2963855133","https://openalex.org/W3065974826","https://openalex.org/W3092527263","https://openalex.org/W3112906266","https://openalex.org/W3113089224","https://openalex.org/W3138516171","https://openalex.org/W3159913668","https://openalex.org/W3167976421","https://openalex.org/W3177096435","https://openalex.org/W3216820735","https://openalex.org/W4287555433","https://openalex.org/W4296549243","https://openalex.org/W4312640568","https://openalex.org/W4312776478","https://openalex.org/W4386083133","https://openalex.org/W4386113255","https://openalex.org/W6728622933","https://openalex.org/W6745891213","https://openalex.org/W6757555829","https://openalex.org/W6784323503","https://openalex.org/W6787640964","https://openalex.org/W6795054371","https://openalex.org/W6796810887","https://openalex.org/W6803950395","https://openalex.org/W6810300553","https://openalex.org/W6810531940","https://openalex.org/W6838637662","https://openalex.org/W6842995896","https://openalex.org/W6849248325"],"related_works":["https://openalex.org/W1980381208","https://openalex.org/W2364594919","https://openalex.org/W2167092671","https://openalex.org/W1861706286","https://openalex.org/W2219338811","https://openalex.org/W2149583853","https://openalex.org/W2143002539","https://openalex.org/W4293472652","https://openalex.org/W2113879121","https://openalex.org/W2158511632"],"abstract_inverted_index":{"Out-of-distribution":[0],"(OOD)":[1],"detection":[2],"plays":[3],"a":[4,25,83,98],"crucial":[5],"role":[6],"in":[7,27],"ensuring":[8],"the":[9,18,28,36,40,47,61,71,75,78,88,112,116],"security":[10],"of":[11,39,49,60,90,115],"neural":[12],"networks.":[13],"Existing":[14],"works":[15],"have":[16],"leveraged":[17],"fact":[19],"that":[20,70],"In-distribution":[21],"(ID)":[22],"samples":[23],"form":[24],"subspace":[26,42,81,118],"feature":[29,80],"space,":[30],"achieving":[31],"state-of-the-art":[32],"(SOTA)":[33],"performance.":[34],"However,":[35],"comprehensive":[37],"characteristics":[38],"ID":[41,62,79,117],"still":[43],"leave":[44],"underexplored.":[45],"Recently,":[46],"discovery":[48],"Neural":[50],"Collapse":[51],"$\\left(":[52],"{\\mathcal{N}\\mathcal{C}}":[53],"\\right)$":[54],"sheds":[55],"light":[56],"on":[57],"novel":[58,99],"properties":[59],"subspace.":[63],"Leveraging":[64],"insight":[65],"from":[66],"$\\mathcal{N}\\mathcal{C}$,":[67],"we":[68,96],"observe":[69],"Principal":[72,106],"Angle":[73,107],"between":[74],"features":[76],"and":[77,119,135,141],"forms":[82],"superior":[84,133],"representation":[85],"for":[86],"measuring":[87],"likelihood":[89],"OOD.":[91],"Building":[92],"upon":[93],"this":[94],"observation,":[95],"propose":[97],"$\\mathcal{N}\\mathcal{C}$-inspired":[100],"OOD":[101,142],"scoring":[102],"function,":[103],"named":[104],"Entropy-enhanced":[105],"(EPA),":[108],"which":[109],"integrates":[110],"both":[111],"global":[113],"characteristic":[114],"its":[120,132],"inner":[121],"property.":[122],"We":[123],"experimentally":[124],"compare":[125],"EPA":[126],"with":[127],"various":[128],"SOTA":[129],"approaches,":[130],"validating":[131],"performance":[134],"robustness":[136],"across":[137],"different":[138],"network":[139],"architectures":[140],"datasets.":[143]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
