{"id":"https://openalex.org/W7116772418","doi":"https://doi.org/10.1109/tpami.2025.3646737","title":"Jo-SNC: Combating Noisy Labels Through Fostering Self- and Neighbor-Consistency","display_name":"Jo-SNC: Combating Noisy Labels Through Fostering Self- and Neighbor-Consistency","publication_year":2025,"publication_date":"2025-12-22","ids":{"openalex":"https://openalex.org/W7116772418","doi":"https://doi.org/10.1109/tpami.2025.3646737","pmid":"https://pubmed.ncbi.nlm.nih.gov/41428907"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2025.3646737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2025.3646737","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5073755558","display_name":"Zeren Sun","orcid":"https://orcid.org/0000-0001-6262-5338"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zeren Sun","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027545344","display_name":"Yazhou Yao","orcid":"https://orcid.org/0000-0002-0337-9410"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yazhou Yao","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065250332","display_name":"Tongliang Liu","orcid":"https://orcid.org/0000-0002-9640-6472"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tongliang Liu","raw_affiliation_strings":["School of Computer Science, Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120961690","display_name":"Zechao Li","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zechao Li","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120980183","display_name":"Fumin Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fumin Shen","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5098121783","display_name":"JinHui TANG","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhui Tang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5073755558"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81295953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"48","issue":"4","first_page":"4708","last_page":"4725"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9829000234603882,"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":0.9829000234603882,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.004399999976158142,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.0007999999797903001,"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/regularization","display_name":"Regularization (linguistics)","score":0.5873000025749207},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5169000029563904},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.49799999594688416},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.4668000042438507},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4643000066280365},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42890000343322754},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4108000099658966},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4043999910354614},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3562000095844269},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.3449999988079071}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7523000240325928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7002999782562256},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5873000025749207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5795999765396118},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5169000029563904},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.49799999594688416},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.4668000042438507},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4643000066280365},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4043999910354614},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3626999855041504},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3449999988079071},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2542000114917755},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2025.3646737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2025.3646737","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:41428907","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41428907","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1514928307","https://openalex.org/W1861492603","https://openalex.org/W1903029394","https://openalex.org/W1972675781","https://openalex.org/W2027444427","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2146950091","https://openalex.org/W2147483361","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2566079294","https://openalex.org/W2570343428","https://openalex.org/W2808711976","https://openalex.org/W2905443329","https://openalex.org/W2948606739","https://openalex.org/W2951821459","https://openalex.org/W2962762068","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2967052791","https://openalex.org/W2967363906","https://openalex.org/W2981952612","https://openalex.org/W2990019157","https://openalex.org/W2998418694","https://openalex.org/W3034185248","https://openalex.org/W3035524453","https://openalex.org/W3035598431","https://openalex.org/W3092251340","https://openalex.org/W3093264957","https://openalex.org/W3093401039","https://openalex.org/W3162368724","https://openalex.org/W3169761117","https://openalex.org/W3171007011","https://openalex.org/W3173874704","https://openalex.org/W3193137605","https://openalex.org/W3202897408","https://openalex.org/W4221161877","https://openalex.org/W4226100662","https://openalex.org/W4246193833","https://openalex.org/W4251481993","https://openalex.org/W4282916794","https://openalex.org/W4282938511","https://openalex.org/W4287947470","https://openalex.org/W4312249250","https://openalex.org/W4312305885","https://openalex.org/W4312601326","https://openalex.org/W4312802153","https://openalex.org/W4313014573","https://openalex.org/W4313022252","https://openalex.org/W4319341372","https://openalex.org/W4367666062","https://openalex.org/W4391541320","https://openalex.org/W4393159311","https://openalex.org/W4401442103","https://openalex.org/W4401683286","https://openalex.org/W4402592961","https://openalex.org/W4402671886","https://openalex.org/W4402951566","https://openalex.org/W4403791501","https://openalex.org/W4415796573"],"related_works":[],"abstract_inverted_index":{"Label":[0],"noise":[1,48],"is":[2],"pervasive":[3],"in":[4,10,46,103],"various":[5,177],"real-world":[6],"scenarios,":[7],"posing":[8],"challenges":[9],"supervised":[11],"deep":[12],"learning.":[13],"Deep":[14],"networks":[15],"are":[16,142],"vulnerable":[17],"to":[18,23,56,84,89,110,125],"such":[19],"label-corrupted":[20],"samples":[21,132,141],"due":[22],"the":[24,86,91,104,112,155,185],"memorization":[25],"effect.":[26],"One":[27],"major":[28],"stream":[29],"of":[30,93,107,114,189],"previous":[31],"methods":[32,42],"concentrates":[33],"on":[34,77,176],"identifying":[35],"clean":[36,97,115,131],"data":[37],"for":[38],"training.":[39],"However,":[40],"these":[41],"often":[43],"neglect":[44],"imbalances":[45],"label":[47,146],"across":[49],"different":[50],"mini-batches":[51],"and":[52,73,79,138,148,171,180,187],"devote":[53],"insufficient":[54],"attention":[55],"out-of-distribution":[57,139],"noisy":[58,140],"data.":[59],"To":[60],"this":[61],"end,":[62],"we":[63,82,153],"propose":[64,83],"a":[65,94,120,161],"noise-robust":[66],"method":[67],"named":[68],"Jo-SNC":[69],"(Joint":[70],"sample":[71,95,109,116],"selection":[72,128],"model":[74,156],"regularization":[75,164],"based":[76],"Self-":[78],"Neighbor-Consistency).":[80],"Specifically,":[81],"employ":[85],"Jensen-Shannon":[87],"divergence":[88],"measure":[90],"\"likelihood\"":[92],"being":[96],"or":[98],"out-of-distribution.":[99],"This":[100],"process":[101],"factors":[102],"nearest":[105],"neighbors":[106],"each":[108],"reinforce":[111],"reliability":[113],"identification.":[117],"We":[118],"design":[119],"self-adaptive,":[121],"data-driven":[122],"thresholding":[123],"scheme":[124],"adjust":[126],"per-class":[127],"thresholds.":[129],"While":[130],"undergo":[133],"conventional":[134],"training,":[135],"detected":[136],"in-distribution":[137],"trained":[143],"following":[144],"partial":[145],"learning":[147],"negative":[149],"learning,":[150],"respectively.":[151],"Finally,":[152],"advance":[154],"performance":[157],"further":[158],"by":[159],"proposing":[160],"triplet":[162],"consistency":[163],"that":[165],"promotes":[166],"self-prediction":[167],"consistency,":[168,170],"neighbor-prediction":[169],"feature":[172],"consistency.":[173],"Extensive":[174],"experiments":[175],"benchmark":[178],"datasets":[179],"comprehensive":[181],"ablation":[182],"studies":[183],"demonstrate":[184],"effectiveness":[186],"superiority":[188],"our":[190],"approach":[191],"over":[192],"existing":[193],"state-of-the-art":[194],"methods.":[195]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-12-22T00:00:00"}
