{"id":"https://openalex.org/W4312754966","doi":"https://doi.org/10.1109/access.2022.3213667","title":"Boosting Out-of-Distribution Image Detection With Epistemic Uncertainty","display_name":"Boosting Out-of-Distribution Image Detection With Epistemic Uncertainty","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312754966","doi":"https://doi.org/10.1109/access.2022.3213667"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3213667","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3213667","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09915595.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09915595.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016313614","display_name":"Dokwan Oh","orcid":"https://orcid.org/0000-0001-9139-8699"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Dokwan Oh","raw_affiliation_strings":["Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110223580","display_name":"Daehyun Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Daehyun Ji","raw_affiliation_strings":["Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067685949","display_name":"Oh\u2010Min Kwon","orcid":"https://orcid.org/0000-0002-4777-7912"},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ohmin Kwon","raw_affiliation_strings":["School of Electrical Engineering, Chungbuk National University, Cheongju, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Chungbuk National University, Cheongju, South Korea","institution_ids":["https://openalex.org/I163753206"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043342026","display_name":"Yoonsuk Hyun","orcid":"https://orcid.org/0000-0001-5047-7139"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yoonsuk Hyun","raw_affiliation_strings":["Department of Mathematics, Inha University, Incheon, South Korea","ORCiD"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Inha University, Incheon, South Korea","institution_ids":["https://openalex.org/I191879574"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016313614"],"corresponding_institution_ids":["https://openalex.org/I2250650973"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.2776,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64478593,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"10","issue":null,"first_page":"109289","last_page":"109298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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":0.9994000196456909,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9968000054359436,"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/computer-science","display_name":"Computer science","score":0.6869533658027649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6032570600509644},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5513373613357544},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5310139060020447},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47306978702545166},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45531731843948364},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4346925914287567},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4279294013977051},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20291048288345337}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6869533658027649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6032570600509644},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5513373613357544},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5310139060020447},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47306978702545166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45531731843948364},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4346925914287567},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4279294013977051},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20291048288345337},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3213667","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3213667","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09915595.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:be3b23e29d6548e4aa0b49fcf89227aa","is_oa":true,"landing_page_url":"https://doaj.org/article/be3b23e29d6548e4aa0b49fcf89227aa","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 109289-109298 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3213667","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3213667","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09915595.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5299999713897705,"display_name":"Peace, Justice and strong institutions"},{"id":"https://metadata.un.org/sdg/10","score":0.5099999904632568,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1061133926","display_name":null,"funder_award_id":"NRF-2020R1A6A1A12047945","funder_id":"https://openalex.org/F4320311687","funder_display_name":"Ministry of Education"},{"id":"https://openalex.org/G1225283220","display_name":null,"funder_award_id":"NRF-2021R","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G3071639259","display_name":null,"funder_award_id":"2021R1","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G342704958","display_name":null,"funder_award_id":"funded","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G6621595095","display_name":null,"funder_award_id":"NRF-2021R1C1C1008929","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G834017027","display_name":null,"funder_award_id":"ACCESS","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G982292920","display_name":null,"funder_award_id":"NRF-20","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321370","display_name":"Inha University","ror":"https://ror.org/01easw929"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312754966.pdf","grobid_xml":"https://content.openalex.org/works/W4312754966.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W967544008","https://openalex.org/W1503398984","https://openalex.org/W1831674524","https://openalex.org/W1983364832","https://openalex.org/W2015563892","https://openalex.org/W2022508996","https://openalex.org/W2108598243","https://openalex.org/W2118377301","https://openalex.org/W2146502635","https://openalex.org/W2158698691","https://openalex.org/W2194321275","https://openalex.org/W2574524600","https://openalex.org/W2741118761","https://openalex.org/W2788907134","https://openalex.org/W2807414627","https://openalex.org/W2883780447","https://openalex.org/W2889625178","https://openalex.org/W2909392985","https://openalex.org/W2923623744","https://openalex.org/W2929049293","https://openalex.org/W2941898360","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2963564844","https://openalex.org/W2964039533","https://openalex.org/W2964980087","https://openalex.org/W2974594474","https://openalex.org/W3096032994","https://openalex.org/W3118608800","https://openalex.org/W6625168331","https://openalex.org/W6638807622","https://openalex.org/W6640425456","https://openalex.org/W6681435938","https://openalex.org/W6728622933","https://openalex.org/W6729756640","https://openalex.org/W6730042731","https://openalex.org/W6735374517","https://openalex.org/W6735443497","https://openalex.org/W6745553787","https://openalex.org/W6745891213","https://openalex.org/W6752760542","https://openalex.org/W6758015486"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2147697413","https://openalex.org/W2154063878","https://openalex.org/W4231274751","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W2571255492"],"abstract_inverted_index":{"Modern":[0],"deep":[1],"neural":[2],"networks":[3],"are":[4,18],"known":[5],"to":[6,20,59,102,110],"generate":[7],"over-confident":[8,105,113],"class":[9],"predictions":[10,101,109],"even":[11],"for":[12,52],"unseen":[13],"samples.":[14,127],"However,":[15],"safety-critical":[16],"applications":[17],"required":[19],"understand":[21],"examples":[22],"that":[23,55],"differ":[24],"from":[25],"the":[26,50,115,120,123,143,148,152,160,163,172],"training":[27],"distribution.":[28],"For":[29],"example,":[30],"an":[31,42,67],"autonomous":[32],"vehicle":[33],"must":[34],"return":[35],"a":[36,53],"instant":[37],"refusal":[38],"feedback":[39],"when":[40],"encountering":[41],"unexpected":[43],"situation.":[44],"The":[45,94],"voice":[46],"secretary":[47],"should":[48],"re-ask":[49],"user":[51],"command":[54],"was":[56],"not":[57,156],"understood":[58],"prevent":[60],"malfunction.":[61],"In":[62,128],"this":[63,133],"paper,":[64],"we":[65,130],"propose":[66],"out-of-distribution":[68,107,126],"sample":[69,100,108],"detection":[70],"algorithm":[71,154],"using":[72,183],"<italic":[73,82],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[74,83],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Uncertainty-based":[75],"Additive":[76],"Fast":[77],"Gradient":[78],"Sign":[79],"Method</i>":[80],"(":[81],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">UA-FGSM</i>":[84],"),":[85],"which":[86],"uses":[87,142],"Monte":[88],"Carlo":[89],"(MC)":[90],"dropout":[91],"during":[92],"backpropagation.":[93],"proposed":[95,153],"uncertainty-based":[96,138],"method":[97,141],"forces":[98],"in-distribution":[99,124,164],"be":[103,111],"more":[104],"and":[106,125,166,192],"less":[112],"in":[114],"pre-trained":[116,149],"model.":[117,150],"This":[118],"boosts":[119],"discrimination":[121],"between":[122],"addition,":[129],"further":[131],"boost":[132],"difference":[134],"by":[135,168],"continuously":[136],"accumulating":[137],"gradients.":[139],"Our":[140],"inherent":[144],"epistemic":[145],"uncertainty":[146],"of":[147,159,162,171],"Therefore,":[151],"does":[155],"require":[157],"knowledge":[158],"domain":[161],"dataset":[165],"works":[167],"simple":[169],"pre-processing":[170],"already":[173],"trained":[174],"model":[175],"without":[176],"any":[177],"re-training.":[178],"We":[179],"demonstrate":[180],"its":[181],"effectiveness":[182],"diverse":[184],"network":[185],"architectures":[186],"on":[187],"various":[188],"popular":[189],"image":[190],"datasets":[191],"noisy":[193],"settings.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}