{"id":"https://openalex.org/W4386596986","doi":"https://doi.org/10.1109/icip49359.2023.10222875","title":"OEST: Outlier Exposure by Simple Transformations for Out-of-Distribution Detection","display_name":"OEST: Outlier Exposure by Simple Transformations for Out-of-Distribution Detection","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386596986","doi":"https://doi.org/10.1109/icip49359.2023.10222875"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222875","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5005765493","display_name":"Yifan Wu","orcid":"https://orcid.org/0000-0003-2708-4657"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Wu","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China","School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China","institution_ids":["https://openalex.org/I141962983"]},{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008695603","display_name":"Songmin Dai","orcid":"https://orcid.org/0000-0002-2048-0748"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songmin Dai","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China","School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China","institution_ids":["https://openalex.org/I141962983"]},{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102554359","display_name":"Dengye Pan","orcid":"https://orcid.org/0009-0008-5982-2531"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dengye Pan","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China","School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China","institution_ids":["https://openalex.org/I141962983"]},{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339621","display_name":"Xiaoqiang Li","orcid":"https://orcid.org/0000-0001-7243-2783"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqiang Li","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China","School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China","institution_ids":["https://openalex.org/I141962983"]},{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I141962983"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12159366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"2170","last_page":"2174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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":0.9958999752998352,"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/outlier","display_name":"Outlier","score":0.910584568977356},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.8367276787757874},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7898659706115723},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.6959956288337708},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.623133659362793},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5576260685920715},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5417819619178772},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4713824689388275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.461772084236145},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4603320062160492},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45233771204948425},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4333673417568207},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.43191128969192505},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35126328468322754},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3476872146129608},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12030419707298279},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09088659286499023}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.910584568977356},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.8367276787757874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7898659706115723},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.6959956288337708},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.623133659362793},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5576260685920715},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5417819619178772},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4713824689388275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.461772084236145},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4603320062160492},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45233771204948425},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4333673417568207},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.43191128969192505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35126328468322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3476872146129608},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12030419707298279},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09088659286499023},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10222875","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8899999856948853,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W967544008","https://openalex.org/W2017745767","https://openalex.org/W2047643928","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2335728318","https://openalex.org/W2732026016","https://openalex.org/W2734358244","https://openalex.org/W2746314669","https://openalex.org/W2785325870","https://openalex.org/W2902986194","https://openalex.org/W2964137095","https://openalex.org/W3089028909","https://openalex.org/W3092527263","https://openalex.org/W3118608800","https://openalex.org/W3129166376","https://openalex.org/W3176709420","https://openalex.org/W3193940683","https://openalex.org/W4287812705","https://openalex.org/W4312331916","https://openalex.org/W6625168331","https://openalex.org/W6703116779","https://openalex.org/W6728622933","https://openalex.org/W6743428213","https://openalex.org/W6747899497","https://openalex.org/W6774314701","https://openalex.org/W6776700526","https://openalex.org/W6780874654","https://openalex.org/W6784323503","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W4308235887"],"abstract_inverted_index":{"Although":[0],"the":[1,17,21,69,72,142],"previous":[2,100],"works":[3],"for":[4,117],"out-of-distribution(OOD)":[5],"detection":[6],"have":[7],"achieved":[8],"great":[9],"improvements,":[10],"they":[11],"are":[12,107],"still":[13],"highly":[14],"dependent":[15],"on":[16],"specific":[18],"selection":[19],"of":[20,42,74,78,93,105],"outliers":[22,70],"from":[23],"external":[24],"datasets":[25],"or":[26],"that":[27,138],"transformed":[28],"by":[29,61,71],"certain":[30],"data":[31,79,97,119],"augmentations,":[32],"and":[33,127],"hence":[34],"cannot":[35],"be":[36],"applied":[37],"in":[38,48,99,129],"a":[39,53],"wide":[40],"range":[41],"domains.":[43],"To":[44],"solve":[45],"this":[46,49],"problem,":[47],"paper,":[50],"we":[51,113],"propose":[52],"simple,":[54],"yet":[55],"effective":[56],"method":[57,140],"called":[58],"Outlier":[59],"Exposure":[60],"Simple":[62],"Transformations":[63],"(OEST),":[64],"which":[65],"aims":[66],"at":[67],"exposing":[68],"composition":[73],"several":[75],"simple":[76,118],"transformations":[77],"augmentations":[80,98],"via":[81],"energy":[82],"score.":[83],"In":[84],"addition,":[85],"our":[86,121,135,139],"training":[87,122],"scheme":[88,123],"can":[89],"make":[90],"full":[91],"use":[92],"nearly":[94],"all":[95],"considered":[96],"works,":[101],"even":[102],"though":[103],"some":[104],"them":[106],"generally":[108],"regarded":[109],"as":[110],"useless.":[111],"And":[112],"also":[114],"find":[115],"that,":[116],"augmentation,":[120],"is":[124],"less":[125],"time-consuming":[126],"better":[128],"performance":[130],"than":[131],"relative":[132],"works.":[133],"Furthermore,":[134],"experiments":[136],"validate":[137],"outperforms":[141],"state-of-the-art":[143],"methods.":[144]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
