{"id":"https://openalex.org/W3157334780","doi":"https://doi.org/10.1109/icpr48806.2021.9412469","title":"Local Clustering with Mean Teacher for Semi-supervised learning","display_name":"Local Clustering with Mean Teacher for Semi-supervised learning","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3157334780","doi":"https://doi.org/10.1109/icpr48806.2021.9412469","mag":"3157334780"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","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/A5101797800","display_name":"Zexi Chen","orcid":"https://orcid.org/0000-0001-9549-9323"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zexi Chen","raw_affiliation_strings":["North Carolina State University, Raleigh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina State University, Raleigh, USA","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063381941","display_name":"B. Dutton","orcid":"https://orcid.org/0000-0003-1272-130X"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Dutton","raw_affiliation_strings":["North Carolina State University, Raleigh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina State University, Raleigh, USA","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018612636","display_name":"Bharathkumar Ramachandra","orcid":"https://orcid.org/0000-0001-9671-6430"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bharathkumar Ramachandra","raw_affiliation_strings":["North Carolina State University, Raleigh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina State University, Raleigh, USA","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091159460","display_name":"Tianfu Wu","orcid":"https://orcid.org/0000-0001-8911-5506"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianfu Wu","raw_affiliation_strings":["North Carolina State University, Raleigh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina State University, Raleigh, USA","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065410886","display_name":"Ranga Raju Vatsavai","orcid":"https://orcid.org/0000-0002-7083-0267"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ranga Raju Vatsavai","raw_affiliation_strings":["Behavioral Reinforcement Learning Lab, Lirio & North Carolina State University, Raleigh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Behavioral Reinforcement Learning Lab, Lirio & North Carolina State University, Raleigh, USA","institution_ids":["https://openalex.org/I137902535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I137902535"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"6243","last_page":"6250"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9983999729156494,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.994700014591217,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7868939638137817},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7352016568183899},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6517415642738342},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.5882962942123413},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5578452944755554},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5421801805496216},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4862288534641266},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4861111640930176},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4780890643596649},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.46238651871681213},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45251938700675964},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.451386034488678},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4297689199447632},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42681241035461426},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4246447682380676}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7868939638137817},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7352016568183899},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6517415642738342},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.5882962942123413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5578452944755554},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5421801805496216},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4862288534641266},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4861111640930176},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4780890643596649},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.46238651871681213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45251938700675964},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.451386034488678},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4297689199447632},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42681241035461426},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4246447682380676},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8299999833106995}],"awards":[{"id":"https://openalex.org/G185237545","display_name":null,"funder_award_id":"IIS-1822477","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3108606023","display_name":null,"funder_award_id":"W911NF1810295","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7500496127","display_name":null,"funder_award_id":"IIS-1909644","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W830076066","https://openalex.org/W1597459461","https://openalex.org/W1886688172","https://openalex.org/W1909320841","https://openalex.org/W1945616565","https://openalex.org/W2095705004","https://openalex.org/W2108501770","https://openalex.org/W2108598243","https://openalex.org/W2113592823","https://openalex.org/W2139823104","https://openalex.org/W2147062276","https://openalex.org/W2154455818","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2205138079","https://openalex.org/W2280377497","https://openalex.org/W2335728318","https://openalex.org/W2530816535","https://openalex.org/W2592691248","https://openalex.org/W2619371851","https://openalex.org/W2621925205","https://openalex.org/W2804344481","https://openalex.org/W2845604977","https://openalex.org/W2901286235","https://openalex.org/W2949416428","https://openalex.org/W2949985837","https://openalex.org/W2951970475","https://openalex.org/W2952229419","https://openalex.org/W2953070460","https://openalex.org/W2963043971","https://openalex.org/W2963080758","https://openalex.org/W2963090522","https://openalex.org/W2963150697","https://openalex.org/W2963170156","https://openalex.org/W2963207607","https://openalex.org/W2963373786","https://openalex.org/W2963558289","https://openalex.org/W2963584589","https://openalex.org/W2964159205","https://openalex.org/W2978426779","https://openalex.org/W2981571980","https://openalex.org/W4300903698","https://openalex.org/W6623329352","https://openalex.org/W6674330103","https://openalex.org/W6675944832","https://openalex.org/W6676772307","https://openalex.org/W6680434193","https://openalex.org/W6682494755","https://openalex.org/W6684191040","https://openalex.org/W6718379498","https://openalex.org/W6733814495","https://openalex.org/W6736346607","https://openalex.org/W6738597727"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2942366970","https://openalex.org/W2807634898","https://openalex.org/W2049382867","https://openalex.org/W2186210338","https://openalex.org/W3134515272","https://openalex.org/W4315703132"],"abstract_inverted_index":{"The":[0,209],"Mean":[1],"Teacher":[2],"(MT)":[3],"model":[4,62,170],"of":[5,29,45,82,93,158,165,200],"Tarvainen":[6],"and":[7,34,179,194],"Valpola":[8],"has":[9],"shown":[10],"good":[11],"performance":[12,195],"on":[13,144,174],"several":[14],"semi-supervised":[15,175,204],"benchmark":[16,176],"datasets.":[17],"MT":[18,49,187,193],"maintains":[19],"a":[20,30,69,139],"teacher":[21,61],"model's":[22,32],"weights":[23,33],"as":[24],"the":[25,36,46,80,127,148,163,198,201],"exponential":[26],"moving":[27],"average":[28],"student":[31],"minimizes":[35],"divergence":[37],"between":[38,130],"their":[39,159,166],"probability":[40],"predictions":[41],"under":[42],"diverse":[43],"perturbations":[44],"inputs.":[47],"However,":[48],"is":[50,90,211],"known":[51],"to":[52,78,103,106,186,192,197],"suffer":[53],"from":[54],"confirmation":[55,83],"bias,":[56],"that":[57,181],"is,":[58],"reinforcing":[59],"incorrect":[60],"predictions.":[63],"In":[64,85],"this":[65],"work,":[66],"we":[67,120],"propose":[68],"simple":[70],"yet":[71],"effective":[72],"method":[73],"called":[74],"Local":[75],"Clustering":[76],"(LC)":[77],"mitigate":[79],"effect":[81],"bias.":[84],"MT,":[86],"each":[87,107],"data":[88,99,122,132,146,151],"point":[89],"considered":[91],"independent":[92],"other":[94,108],"points":[95,100,123,133,152],"during":[96],"training;":[97],"however,":[98],"are":[101,153],"likely":[102],"be":[104],"close":[105],"in":[109,134,203],"feature":[110,135],"space":[111],"if":[112],"they":[113],"share":[114],"similar":[115],"features.":[116],"Motivated":[117],"by":[118,125],"this,":[119],"cluster":[121],"locally":[124],"minimizing":[126],"pairwise":[128],"distance":[129],"neighboring":[131],"space.":[136],"Combined":[137],"with":[138,162],"standard":[140],"classification":[141],"cross-entropy":[142],"objective":[143],"labeled":[145],"points,":[147],"misclassified":[149],"unlabeled":[150],"pulled":[154],"towards":[155],"high-density":[156],"regions":[157],"correct":[160],"class":[161],"help":[164],"neighbors,":[167],"thus":[168],"improving":[169],"performance.":[171],"We":[172],"demonstrate":[173],"datasets":[177],"SVHN":[178],"CIFAR-10":[180],"adding":[182],"our":[183],"LC":[184],"loss":[185],"yields":[188],"significant":[189],"improvements":[190],"compared":[191],"comparable":[196],"state":[199],"art":[202],"learning":[205],"<sup":[206],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[207],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">11</sup>":[208],"code":[210],"available":[212],"at:":[213],"https://github.com/jay1204/local_clustering_with_mt_for_ssl.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}