{"id":"https://openalex.org/W3199390827","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533899","title":"Entropic Out-of-Distribution Detection","display_name":"Entropic Out-of-Distribution Detection","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3199390827","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533899","mag":"3199390827"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533899","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1908.05569","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021701067","display_name":"David Mac\u00eado","orcid":"https://orcid.org/0000-0002-2527-4548"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]},{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]},{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["BR","CA"],"is_corresponding":false,"raw_author_name":"David Macedo","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil","Montreal Institute for Learning Algorithms, University of Montreal, Quebec, Canada"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I25112270"]},{"raw_affiliation_string":"Montreal Institute for Learning Algorithms, University of Montreal, Quebec, Canada","institution_ids":["https://openalex.org/I60158472","https://openalex.org/I70931966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022457755","display_name":"Tsang Ing Ren","orcid":"https://orcid.org/0000-0002-3677-0264"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Tsang Ing Ren","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086345001","display_name":"Cleber Zanchettin","orcid":"https://orcid.org/0000-0001-6421-9747"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]},{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["BR","US"],"is_corresponding":false,"raw_author_name":"Cleber Zanchettin","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil","Northwestern University, Evanston, United States of America"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I25112270"]},{"raw_affiliation_string":"Northwestern University, Evanston, United States of America","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100615438","display_name":"Adriano L. I. Oliveira","orcid":"https://orcid.org/0000-0002-5614-229X"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Adriano L. I. Oliveira","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025550530","display_name":"Teresa B. Ludermir","orcid":"https://orcid.org/0000-0002-8980-6742"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Teresa Ludermir","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife, Brasil","institution_ids":["https://openalex.org/I25112270"]}]}],"institution_assertions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.136,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":22,"citation_normalized_percentile":{"value":0.999914,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998,"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":0.9998,"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":0.9995,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9843,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.92412937},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6758847}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.92412937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.68712485},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6758847},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5496968},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5066641},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5040556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5004995},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4792728},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37605304},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3700102},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3464925}],"mesh":[],"locations_count":3,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533899","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1908.05569","pdf_url":"https://arxiv.org/pdf/1908.05569","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.1908.05569","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1908.05569","pdf_url":"https://arxiv.org/pdf/1908.05569","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.88}],"grants":[],"datasets":[],"versions":["https://openalex.org/W3106636111","https://openalex.org/W3199390827"],"referenced_works_count":54,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1677182931","https://openalex.org/W1917989004","https://openalex.org/W2015563892","https://openalex.org/W2018459374","https://openalex.org/W2032558547","https://openalex.org/W2099111195","https://openalex.org/W2108598243","https://openalex.org/W2119880843","https://openalex.org/W2302255633","https://openalex.org/W2335728318","https://openalex.org/W2478708596","https://openalex.org/W2520774990","https://openalex.org/W2531327146","https://openalex.org/W2600383743","https://openalex.org/W2626967530","https://openalex.org/W2737498590","https://openalex.org/W2767414122","https://openalex.org/W2786712888","https://openalex.org/W2788907134","https://openalex.org/W2810469995","https://openalex.org/W2867167548","https://openalex.org/W2890884881","https://openalex.org/W2904981516","https://openalex.org/W2913848079","https://openalex.org/W2951883849","https://openalex.org/W2951965145","https://openalex.org/W2952140516","https://openalex.org/W2963081736","https://openalex.org/W2963215553","https://openalex.org/W2963446712","https://openalex.org/W2963656735","https://openalex.org/W2963693742","https://openalex.org/W2963995504","https://openalex.org/W2964116600","https://openalex.org/W2964212410","https://openalex.org/W2969985801","https://openalex.org/W2970121940","https://openalex.org/W2970317235","https://openalex.org/W2970946347","https://openalex.org/W2990064013","https://openalex.org/W2992308087","https://openalex.org/W3020972252","https://openalex.org/W3034230713","https://openalex.org/W3034370310","https://openalex.org/W3092527263","https://openalex.org/W3102616566","https://openalex.org/W3103152812","https://openalex.org/W3108123919","https://openalex.org/W3118608800","https://openalex.org/W4252028749","https://openalex.org/W4288363925","https://openalex.org/W572355794","https://openalex.org/W967544008"],"related_works":["https://openalex.org/W4303493643","https://openalex.org/W4287591324","https://openalex.org/W4226420367","https://openalex.org/W3108503355","https://openalex.org/W3107204728","https://openalex.org/W3090555870","https://openalex.org/W3022820045","https://openalex.org/W2980176872","https://openalex.org/W2962876041","https://openalex.org/W2912971006"],"abstract_inverted_index":{"Out-of-distribution":[0],"(OOD)":[1],"detection":[2,104,138,145,166],"approaches":[3],"usually":[4],"present":[5],"special":[6],"requirements":[7],"(e.g.,":[8,19],"hyperparameter":[9,73,130],"validation,":[10],"collection":[11,71,124],"of":[12,34,60,125],"outlier":[13,126],"data)":[14],"and":[15,39,52,84,128],"produce":[16,85],"side":[17],"effects":[18],"classification":[20,81,120],"accuracy":[21,82,121],"drop,":[22,122],"slower":[23],"energy-inefficient":[24],"inferences).":[25],"We":[26],"argue":[27],"that":[28,93],"these":[29],"issues":[30],"are":[31],"a":[32],"consequence":[33],"the":[35,42,49,53,61,113,135,149,153,161],"SoftMax":[36,62,150],"loss":[37,51,63,66,99,108,151,155],"anisotropy":[38],"disagreement":[40],"with":[41,97,152],"maximum":[43],"entropy":[44],"principle.":[45],"Thus,":[46],"we":[47,133],"propose":[48],"IsoMax":[50,65,98,107,154],"entropic":[54],"score.":[55],"The":[56,75,106],"seamless":[57,136],"drop-in":[58],"replacement":[59],"by":[64],"requires":[67],"neither":[68],"additional":[69],"data":[70],"nor":[72],"validation.":[74],"trained":[76],"models":[77],"do":[78],"not":[79],"exhibit":[80],"drop":[83],"fast":[86],"energy-efficient":[87,117],"inferences.":[88],"Moreover,":[89],"our":[90],"experiments":[91],"show":[92],"training":[94],"neural":[95],"networks":[96],"significantly":[100],"improves":[101],"their":[102,158],"OOD":[103,137,144,165],"performance.":[105],"exhibits":[109],"state-of-the-art":[110],"performance":[111,159],"under":[112],"mentioned":[114],"conditions":[115],"(fast":[116],"inference,":[118],"no":[119,123,129],"data,":[127],"validation),":[131],"which":[132],"call":[134],"task.":[139],"In":[140],"future":[141],"work,":[142],"current":[143],"methods":[146],"may":[147],"replace":[148],"to":[156],"improve":[157],"on":[160],"commonly":[162],"studied":[163],"non-seamless":[164],"problem.":[167]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3199390827","counts_by_year":[{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-01-04T16:10:41.870878","created_date":"2021-09-27"}