{"id":"https://openalex.org/W3189709610","doi":"https://doi.org/10.1109/ur52253.2021.9494630","title":"Selection of Class-conditional Filters for Semantic Shifted OOD Detection","display_name":"Selection of Class-conditional Filters for Semantic Shifted OOD Detection","publication_year":2021,"publication_date":"2021-07-12","ids":{"openalex":"https://openalex.org/W3189709610","doi":"https://doi.org/10.1109/ur52253.2021.9494630","mag":"3189709610"},"language":"en","primary_location":{"id":"doi:10.1109/ur52253.2021.9494630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ur52253.2021.9494630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Conference on Ubiquitous Robots (UR)","raw_type":"proceedings-article"},"type":"article","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/A5073816488","display_name":"Yeonguk Yu","orcid":"https://orcid.org/0000-0003-2147-4718"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yeonguk Yu","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea"],"affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100705459","display_name":"Sungho Shin","orcid":"https://orcid.org/0000-0002-6155-7449"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungho Shin","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea"],"affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100356563","display_name":"JongWon Kim","orcid":"https://orcid.org/0009-0009-0968-3104"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongwon Kim","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea"],"affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031483606","display_name":"Kyoobin Lee","orcid":"https://orcid.org/0000-0003-4299-4923"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyoobin Lee","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea"],"affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), South Korea","institution_ids":["https://openalex.org/I39534123"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073816488"],"corresponding_institution_ids":["https://openalex.org/I39534123"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09132353,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"70","issue":null,"first_page":"43","last_page":"47"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9980000257492065,"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":0.9972000122070312,"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":0.7445565462112427},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5764532685279846},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5497314929962158},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5444025993347168},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5332144498825073},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.523324728012085},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5123370885848999},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4923516511917114},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4850577414035797},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.480468213558197},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45756015181541443},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45341619849205017},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.43968862295150757},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.43696245551109314},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.43014150857925415},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.42656978964805603},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40610846877098083},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39003539085388184},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3858814835548401},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11953422427177429},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.0909176766872406}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7445565462112427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5764532685279846},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5497314929962158},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5444025993347168},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5332144498825073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.523324728012085},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5123370885848999},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4923516511917114},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4850577414035797},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.480468213558197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45756015181541443},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45341619849205017},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.43968862295150757},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.43696245551109314},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.43014150857925415},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.42656978964805603},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40610846877098083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39003539085388184},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3858814835548401},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11953422427177429},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0909176766872406},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ur52253.2021.9494630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ur52253.2021.9494630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Conference on Ubiquitous Robots (UR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W967544008","https://openalex.org/W1522301498","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2335728318","https://openalex.org/W2475287302","https://openalex.org/W2519210008","https://openalex.org/W2531327146","https://openalex.org/W2626967530","https://openalex.org/W2767414122","https://openalex.org/W2867167548","https://openalex.org/W2891899936","https://openalex.org/W2962858109","https://openalex.org/W2963693742","https://openalex.org/W2964121744","https://openalex.org/W2964212410","https://openalex.org/W2975651437","https://openalex.org/W3034217686","https://openalex.org/W3034230713","https://openalex.org/W3034370310","https://openalex.org/W3118608800","https://openalex.org/W6625168331","https://openalex.org/W6631190155","https://openalex.org/W6703116779","https://openalex.org/W6728622933","https://openalex.org/W6739651123","https://openalex.org/W6745891213","https://openalex.org/W6752760542","https://openalex.org/W6766263406","https://openalex.org/W6779395778","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2964954556","https://openalex.org/W3019910406","https://openalex.org/W2969228573","https://openalex.org/W2952813363","https://openalex.org/W2911497689","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"have":[3,28],"been":[4,201],"deployed":[5],"in":[6,110,166,203],"a":[7],"wide":[8],"range":[9],"of":[10,36,65,124,127,187],"applications":[11],"with":[12,20],"remarkable":[13],"performance":[14,182],"but":[15],"can":[16,206],"be":[17,164,207],"easily":[18],"fooled":[19],"data":[21],"that":[22,98,139,176,199],"are":[23],"out-of-distribution":[24],"(OOD).":[25],"Recent":[26],"works":[27],"proposed":[29,132],"detection":[30,96],"methods":[31,59],"for":[32,47,54,61,77,85,183],"OOD":[33,55,62,86,95,142,184],"benchmarks":[34,63,185],"consisting":[35,64,186],"small":[37],"image":[38,66],"datasets":[39,67],"from":[40,68,144,170],"separate":[41],"domains":[42],"(i.e.":[43,72],"object":[44],"classification":[45,52,75,83],"dataset":[46,53,76,84],"training":[48,78],"samples":[49,79],"and":[50,80,115],"digit":[51],"samples).":[56,87],"However,":[57],"these":[58],"fail":[60],"the":[69,111,119,125,145,148,167,180,188],"same":[70,146],"domain":[71],"Korean":[73],"food":[74,82],"Italian":[81],"To":[88],"solve":[89],"this":[90,197],"issue,":[91,198],"we":[92],"propose":[93],"an":[94,141],"framework":[97,133],"utilizes":[99],"two":[100],"simple":[101],"operations:":[102],"counting":[103],"to":[104,117],"find":[105],"class-wise":[106],"highly":[107,128,171],"activated":[108,129,172],"filters":[109],"last":[112],"convolution":[113],"layer,":[114],"summation":[116,123],"calculate":[118],"confidence":[120],"score":[121],"by":[122],"activation":[126],"filters.":[130,173],"The":[131],"is":[134],"based":[135],"on":[136,195],"our":[137,177],"assumption":[138],"given":[140],"sample":[143],"domain,":[147],"CNN":[149],"model":[150],"will":[151],"produce":[152],"similar":[153],"feature":[154,168],"maps":[155],"through":[156],"all":[157],"its":[158],"filters,":[159],"while":[160],"some":[161],"differences":[162],"might":[163],"found":[165],"map":[169],"We":[174],"show":[175],"method":[178],"achieves":[179],"highest":[181],"Food101":[189],"dataset,":[190],"which":[191],"provides":[192],"meaningful":[193],"insights":[194],"how":[196],"has":[200],"encountered":[202],"recent":[204],"works,":[205],"effectively":[208],"addressed.":[209]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
