{"id":"https://openalex.org/W3193689914","doi":"https://doi.org/10.23919/mva51890.2021.9511396","title":"Estimating Contribution of Training Datasets using Shapley Values in Data-scale for Visual Recognition","display_name":"Estimating Contribution of Training Datasets using Shapley Values in Data-scale for Visual Recognition","publication_year":2021,"publication_date":"2021-07-25","ids":{"openalex":"https://openalex.org/W3193689914","doi":"https://doi.org/10.23919/mva51890.2021.9511396","mag":"3193689914"},"language":"en","primary_location":{"id":"doi:10.23919/mva51890.2021.9511396","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mva51890.2021.9511396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 17th International Conference on Machine Vision and Applications (MVA)","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/A5064214884","display_name":"Takayuki Semitsu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133125","display_name":"Mitsubishi Electric (Japan)","ror":"https://ror.org/033y26782","country_code":"JP","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takayuki Semitsu","raw_affiliation_strings":["Mitsubishi Electric Corporation, Kamakura, Japan"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Corporation, Kamakura, Japan","institution_ids":["https://openalex.org/I4210133125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102326061","display_name":"Mitsuki Nakamura","orcid":"https://orcid.org/0009-0005-4549-9313"},"institutions":[{"id":"https://openalex.org/I4210133125","display_name":"Mitsubishi Electric (Japan)","ror":"https://ror.org/033y26782","country_code":"JP","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mitsuki Nakamura","raw_affiliation_strings":["Mitsubishi Electric Corporation, Kamakura, Japan"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Corporation, Kamakura, Japan","institution_ids":["https://openalex.org/I4210133125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012201266","display_name":"Shotaro Ishigami","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133125","display_name":"Mitsubishi Electric (Japan)","ror":"https://ror.org/033y26782","country_code":"JP","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shotaro Ishigami","raw_affiliation_strings":["Mitsubishi Electric Corporation, Kamakura, Japan"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Corporation, Kamakura, Japan","institution_ids":["https://openalex.org/I4210133125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044568397","display_name":"Toru Aoki","orcid":"https://orcid.org/0000-0002-6107-3962"},"institutions":[{"id":"https://openalex.org/I4210133125","display_name":"Mitsubishi Electric (Japan)","ror":"https://ror.org/033y26782","country_code":"JP","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toru Aoki","raw_affiliation_strings":["Mitsubishi Electric Corporation, Kamakura, Japan"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Corporation, Kamakura, Japan","institution_ids":["https://openalex.org/I4210133125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028237720","display_name":"Teng\u2010Yok Lee","orcid":"https://orcid.org/0000-0003-1001-381X"},"institutions":[{"id":"https://openalex.org/I4210133125","display_name":"Mitsubishi Electric (Japan)","ror":"https://ror.org/033y26782","country_code":"JP","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Teng-Yok Lee","raw_affiliation_strings":["Mitsubishi Electric Corporation, Kamakura, Japan"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Corporation, Kamakura, Japan","institution_ids":["https://openalex.org/I4210133125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108216049","display_name":"Yoshimi Isu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133125","display_name":"Mitsubishi Electric (Japan)","ror":"https://ror.org/033y26782","country_code":"JP","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshimi Isu","raw_affiliation_strings":["Mitsubishi Electric Corporation, Kamakura, Japan"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Corporation, Kamakura, Japan","institution_ids":["https://openalex.org/I4210133125"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5064214884"],"corresponding_institution_ids":["https://openalex.org/I4210133125"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48566993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9994999766349792,"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":0.9973000288009644,"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/normalization","display_name":"Normalization (sociology)","score":0.7387200593948364},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7069522142410278},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6234095692634583},{"id":"https://openalex.org/keywords/shapley-value","display_name":"Shapley value","score":0.6076911091804504},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5914679169654846},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5465757846832275},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5293339490890503},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34949201345443726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3424661159515381},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34157973527908325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22903653979301453},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.06646728515625}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7387200593948364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7069522142410278},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6234095692634583},{"id":"https://openalex.org/C199022921","wikidata":"https://www.wikidata.org/wiki/Q240046","display_name":"Shapley value","level":3,"score":0.6076911091804504},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5914679169654846},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5465757846832275},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5293339490890503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34949201345443726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3424661159515381},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34157973527908325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22903653979301453},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.06646728515625},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/mva51890.2021.9511396","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mva51890.2021.9511396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 17th International Conference on Machine Vision and Applications (MVA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W37721291","https://openalex.org/W1562353621","https://openalex.org/W1596233070","https://openalex.org/W2047632871","https://openalex.org/W2129888542","https://openalex.org/W2204750386","https://openalex.org/W2487898712","https://openalex.org/W2736410039","https://openalex.org/W2921189944","https://openalex.org/W2939984132","https://openalex.org/W2949574275","https://openalex.org/W2962843773","https://openalex.org/W2962862931","https://openalex.org/W2981684125","https://openalex.org/W2984145721","https://openalex.org/W3001279689","https://openalex.org/W6737947904","https://openalex.org/W6760064560","https://openalex.org/W6762234667","https://openalex.org/W6769981643"],"related_works":["https://openalex.org/W2016839265","https://openalex.org/W2018445155","https://openalex.org/W2026355170","https://openalex.org/W2946038180","https://openalex.org/W2508457823","https://openalex.org/W1863050261","https://openalex.org/W2058991683","https://openalex.org/W2626335822","https://openalex.org/W2071846378","https://openalex.org/W2185993203"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,109],"propose":[4],"a":[5,16,68,179],"method":[6,27,52,94],"to":[7,20,37,57,89],"measure":[8,38],"contributions":[9,66,99,122,143],"of":[10,23,33,42,45,80,86,98,118,123,163,172,178],"multiple":[11],"datasets":[12],"i.e.":[13],"how":[14],"much":[15],"specific":[17],"dataset":[18,180],"contributes":[19],"improve":[21],"accuracy":[22,44,56,150],"the":[24,43,46,55,84,91,106,129,135,142],"model.":[25],"Our":[26,93],"is":[28,36],"based":[29],"on":[30],"shapley":[31,156,164],"value,":[32],"which":[34,174],"purpose":[35],"contribution":[39],"by":[40,77,83,168],"difference":[41],"models.":[47],"Unlike":[48],"previous":[49],"method,":[50,108],"our":[51],"first":[53],"converts":[54],"data-scale":[58,167],"measurements":[59],"using":[60],"fitted":[61],"log":[62],"curve.":[63],"We":[64,159],"calculate":[65],"in":[67,100,137,166],"fair":[69],"way":[70],"that":[71,128],"each":[72,124],"trials":[73],"are":[74],"evaluated":[75],"not":[76],"its":[78,169],"improvements":[79],"accuracy,":[81],"but":[82],"number":[85,171],"data":[87,102,139],"needed":[88],"make":[90],"improvements.":[92],"can":[95,132],"avoid":[96],"overestimation":[97],"small":[101,138],"cases.":[103],"To":[104],"evaluate":[105],"proposed":[107,130,161],"trained":[110],"models":[111],"for":[112],"Person":[113],"Re-Identification":[114],"tasks":[115],"with":[116],"combinations":[117],"datasets,":[119],"and":[120,151],"calculated":[121],"datasets.":[125],"Results":[126],"show":[127],"metrics":[131],"effectively":[133],"reduce":[134],"over-estimations":[136],"cases,":[140],"while":[141],"maintain":[144],"good":[145],"properties":[146],"such":[147],"as":[148],"local":[149],"additive":[152],"law":[153],"derived":[154],"from":[155],"value":[157],"definition.":[158],"also":[160],"normalization":[162],"values":[165],"actual":[170],"instances,":[173],"indicates":[175],"intrinsic":[176],"importance":[177],"per":[181],"instance.":[182]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}