{"id":"https://openalex.org/W4411725638","doi":"https://doi.org/10.1109/tnnls.2025.3580892","title":"Inducing Neural Collapse via Anticlasses and One-Cold Cross-Entropy Loss","display_name":"Inducing Neural Collapse via Anticlasses and One-Cold Cross-Entropy Loss","publication_year":2025,"publication_date":"2025-06-27","ids":{"openalex":"https://openalex.org/W4411725638","doi":"https://doi.org/10.1109/tnnls.2025.3580892","pmid":"https://pubmed.ncbi.nlm.nih.gov/40577297"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3580892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3580892","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5106203262","display_name":"Dimitrios Katsikas","orcid":null},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Dimitrios Katsikas","raw_affiliation_strings":["Department of Informatics, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece"],"raw_orcid":"https://orcid.org/0009-0007-3903-3415","affiliations":[{"raw_affiliation_string":"Department of Informatics, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061050264","display_name":"Nikolaos Passalis","orcid":"https://orcid.org/0000-0003-1177-9139"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Nikolaos Passalis","raw_affiliation_strings":["Department of Chemical Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece"],"raw_orcid":"https://orcid.org/0000-0003-1177-9139","affiliations":[{"raw_affiliation_string":"Department of Chemical Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041054091","display_name":"Anastasios Tefas","orcid":"https://orcid.org/0000-0003-1288-3667"},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Anastasios Tefas","raw_affiliation_strings":["Department of Informatics, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece"],"raw_orcid":"https://orcid.org/0000-0003-1288-3667","affiliations":[{"raw_affiliation_string":"Department of Informatics, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21370196"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12631258,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"36","issue":"10","first_page":"18133","last_page":"18144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.295199990272522,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.295199990272522,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10301","display_name":"Mitochondrial Function and Pathology","score":0.28209999203681946,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.5317589044570923},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.2960510849952698},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.26687514781951904},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.16397225856781006}],"concepts":[{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.5317589044570923},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.2960510849952698},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.26687514781951904},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.16397225856781006}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3580892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3580892","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:40577297","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40577297","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1821462560","https://openalex.org/W1932198206","https://openalex.org/W2108598243","https://openalex.org/W2119880843","https://openalex.org/W2144578941","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2558748708","https://openalex.org/W2566079294","https://openalex.org/W2963149653","https://openalex.org/W2963163009","https://openalex.org/W2963314614","https://openalex.org/W2963446712","https://openalex.org/W2964137095","https://openalex.org/W2964184826","https://openalex.org/W2965373594","https://openalex.org/W2981952612","https://openalex.org/W3018252856","https://openalex.org/W3030901202","https://openalex.org/W3065974826","https://openalex.org/W3095099350","https://openalex.org/W3097217077","https://openalex.org/W3120093105","https://openalex.org/W3132598136","https://openalex.org/W3134961575","https://openalex.org/W3138516171","https://openalex.org/W3189075232","https://openalex.org/W3199097846","https://openalex.org/W3199247446","https://openalex.org/W3205278400","https://openalex.org/W4315473654","https://openalex.org/W4402716128","https://openalex.org/W4409494656"],"related_works":["https://openalex.org/W2951959408","https://openalex.org/W3214636190","https://openalex.org/W2360901323","https://openalex.org/W4297833327","https://openalex.org/W4281705050","https://openalex.org/W2895831313","https://openalex.org/W4389779252","https://openalex.org/W4289406078","https://openalex.org/W4404200101","https://openalex.org/W3037097571"],"abstract_inverted_index":{"While":[0],"softmax":[1],"cross-entropy":[2],"(CE)":[3],"loss":[4,41,173],"is":[5,72],"the":[6,16,20,23,53,76,100,111,122,142,147,156,175],"standard":[7],"objective":[8,177],"for":[9,60,117],"supervised":[10],"classification,":[11,185],"it":[12],"primarily":[13],"focuses":[14],"on":[15],"ground-truth":[17],"classes,":[18],"ignoring":[19],"relationships":[21],"between":[22],"nontarget,":[24],"complementary":[25,57,81,112],"classes.":[26,58,131],"This":[27,132],"leaves":[28],"valuable":[29],"information":[30],"unexploited":[31],"during":[32,153],"optimization.":[33],"In":[34],"this":[35,49],"work,":[36],"we":[37,63,120],"propose":[38],"a":[39,105,115,135],"novel":[40],"function,":[42],"one-cold":[43,107],"CE":[44],"(OCCE)":[45],"loss,":[46],"which":[47,67],"addresses":[48,155],"limitation":[50],"by":[51],"structuring":[52],"activations":[54,127],"of":[55,69,75,139,149,160],"these":[56],"Specifically,":[59],"each":[61,118],"class,":[62],"define":[64],"an":[65],"anticlass,":[66,119],"consists":[68],"everything":[70],"that":[71,95,170],"not":[73,97],"part":[74],"target":[77,116],"class-this":[78],"includes":[79],"all":[80,129],"classes":[82,113,140],"as":[83,85,114],"well":[84],"out-of-distribution":[86],"(OOD)":[87],"samples,":[88],"noise,":[89],"or":[90],"in":[91,141,174],"general":[92],"any":[93],"instance":[94],"does":[96],"belong":[98],"to":[99,124],"true":[101],"class.":[102],"By":[103],"setting":[104],"uniform":[106],"encoded":[108],"distribution":[109],"over":[110],"encourage":[121],"model":[123],"equally":[125],"distribute":[126],"across":[128,181],"nontarget":[130],"approach":[133],"promotes":[134],"symmetric":[136],"geometric":[137],"structure":[138],"final":[143],"feature":[144],"space,":[145],"increases":[146],"degree":[148],"neural":[150,161],"collapse":[151],"(NC)":[152],"training,":[154],"independence":[157],"deficit":[158],"problem":[159],"networks,":[162],"and":[163,188],"improves":[164],"generalization.":[165],"Our":[166],"extensive":[167],"evaluation":[168],"shows":[169],"incorporating":[171],"OCCE":[172],"optimization":[176],"consistently":[178],"enhances":[179],"performance":[180],"multiple":[182],"settings,":[183],"including":[184],"open-set":[186],"recognition,":[187],"OOD":[189],"detection.":[190]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
