{"id":"https://openalex.org/W4296916831","doi":"https://doi.org/10.1109/tnnls.2022.3202752","title":"A Convergence Path to Deep Learning on Noisy Labels","display_name":"A Convergence Path to Deep Learning on Noisy Labels","publication_year":2022,"publication_date":"2022-09-22","ids":{"openalex":"https://openalex.org/W4296916831","doi":"https://doi.org/10.1109/tnnls.2022.3202752","pmid":"https://pubmed.ncbi.nlm.nih.gov/36136922"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3202752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3202752","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","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/A5038147807","display_name":"Defu Liu","orcid":"https://orcid.org/0000-0001-9000-7262"},"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":"Defu Liu","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-9000-7262","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":"middle","author":{"id":"https://openalex.org/A5021751767","display_name":"Ivor W. Tsang","orcid":"https://orcid.org/0000-0001-8095-4637"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ivor W. Tsang","raw_affiliation_strings":["Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore","institution_ids":["https://openalex.org/I115228651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012354366","display_name":"Guowu Yang","orcid":"https://orcid.org/0000-0002-5133-0320"},"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":"Guowu Yang","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-5133-0320","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"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1605,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81284572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"35","issue":"4","first_page":"5170","last_page":"5182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9750000238418579,"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/robustness","display_name":"Robustness (evolution)","score":0.7549183368682861},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7283282279968262},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6732288002967834},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6686331033706665},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6132790446281433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5858265161514282},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5851166248321533},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49753716588020325},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.48730623722076416},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4791231155395508},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47508683800697327},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.42074671387672424},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3576735854148865}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7549183368682861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7283282279968262},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6732288002967834},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6686331033706665},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6132790446281433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5858265161514282},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5851166248321533},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49753716588020325},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.48730623722076416},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4791231155395508},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47508683800697327},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.42074671387672424},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3576735854148865},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","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},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3202752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3202752","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:36136922","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36136922","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 neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8950979096","display_name":null,"funder_award_id":"62172075","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":77,"referenced_works":["https://openalex.org/W72116547","https://openalex.org/W369786348","https://openalex.org/W1969623397","https://openalex.org/W1975128126","https://openalex.org/W1982032418","https://openalex.org/W1998034343","https://openalex.org/W2032708784","https://openalex.org/W2121056381","https://openalex.org/W2127452375","https://openalex.org/W2167460663","https://openalex.org/W2194775991","https://openalex.org/W2285660444","https://openalex.org/W2312481402","https://openalex.org/W2565516711","https://openalex.org/W2566079294","https://openalex.org/W2587219380","https://openalex.org/W2682189153","https://openalex.org/W2795282075","https://openalex.org/W2807945348","https://openalex.org/W2808448059","https://openalex.org/W2916134051","https://openalex.org/W2963028646","https://openalex.org/W2963150697","https://openalex.org/W2963697299","https://openalex.org/W2964155802","https://openalex.org/W2964159205","https://openalex.org/W2964273174","https://openalex.org/W2964292098","https://openalex.org/W2965147845","https://openalex.org/W2978625989","https://openalex.org/W2981873476","https://openalex.org/W2981952041","https://openalex.org/W2997519153","https://openalex.org/W3003265726","https://openalex.org/W3035546924","https://openalex.org/W3093903225","https://openalex.org/W3107308595","https://openalex.org/W3118608800","https://openalex.org/W3126886311","https://openalex.org/W3154173867","https://openalex.org/W3156419234","https://openalex.org/W3156507707","https://openalex.org/W3157898411","https://openalex.org/W3158069950","https://openalex.org/W3163199981","https://openalex.org/W3199656532","https://openalex.org/W6623329352","https://openalex.org/W6634797541","https://openalex.org/W6639331287","https://openalex.org/W6678280073","https://openalex.org/W6683235360","https://openalex.org/W6712300348","https://openalex.org/W6729244039","https://openalex.org/W6738471490","https://openalex.org/W6738532226","https://openalex.org/W6743885473","https://openalex.org/W6747898760","https://openalex.org/W6750523955","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6751661576","https://openalex.org/W6751883947","https://openalex.org/W6755625343","https://openalex.org/W6757248479","https://openalex.org/W6758632346","https://openalex.org/W6760993166","https://openalex.org/W6761059766","https://openalex.org/W6762563763","https://openalex.org/W6762913911","https://openalex.org/W6770366593","https://openalex.org/W6771936042","https://openalex.org/W6772389607","https://openalex.org/W6773116539","https://openalex.org/W6774313938","https://openalex.org/W6778931815","https://openalex.org/W6779784029","https://openalex.org/W6780619371"],"related_works":["https://openalex.org/W3210882018","https://openalex.org/W2970990331","https://openalex.org/W4379116102","https://openalex.org/W3207178610","https://openalex.org/W3211782752","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3000197790","https://openalex.org/W4315865067"],"abstract_inverted_index":{"In":[0,137],"many":[1],"real-world":[2],"machine":[3],"learning":[4],"classification":[5],"applications,":[6],"the":[7,27,46,50,57,74,81,85,117,122,145,153,161,177,196],"model":[8,72,90],"performance":[9],"based":[10,143],"on":[11,36,116,144,152],"deep":[12,123],"neural":[13],"networks":[14],"(DNNs)":[15],"oftentimes":[16],"suffers":[17],"from":[18,111],"label":[19,42,77,126,205],"noise.":[20,206],"Various":[21],"methods":[22],"have":[23],"been":[24],"proposed":[25,146],"in":[26,73,160,180],"literature":[28],"to":[29,64,98,108,171],"address":[30],"this":[31,181],"issue,":[32],"primarily":[33],"by":[34],"focusing":[35],"designing":[37],"noise-tolerant":[38,51],"loss":[39,52,103],"functions,":[40],"cleaning":[41],"noise,":[43,78],"and":[44,79,130,156,175,192],"correcting":[45],"objective":[47],"loss.":[48],"However,":[49],"functions":[53],"face":[54],"challenges":[55],"when":[56],"noise":[58,127,173],"level":[59],"increases.":[60],"This":[61],"article":[62],"aims":[63],"reveal":[65],"a":[66,70,88,96,133],"convergence":[67,82,119],"path":[68,83,120],"of":[69,76,87,135,198,204],"trained":[71,89],"presence":[75],"here,":[80],"depicts":[84],"evolution":[86],"over":[91],"epochs.":[92],"We":[93,164],"first":[94],"propose":[95],"theorem":[97],"demonstrate":[99],"that":[100,148],"any":[101],"surrogate":[102],"function":[104],"can":[105],"be":[106],"used":[107],"learn":[109],"DNNs":[110],"noisy":[112,154],"labels.":[113],"Next,":[114],"theories":[115],"general":[118],"for":[121],"models":[124],"under":[125,201],"are":[128],"presented":[129,179],"verified":[131],"through":[132],"series":[134],"experiments.":[136],"addition,":[138],"we":[139],"design":[140],"an":[141],"algorithm":[142],"theorems":[147,178],"make":[149],"efficient":[150],"corrections":[151],"labels":[155],"achieve":[157],"strong":[158],"robustness":[159],"DNN":[162],"models.":[163],"designed":[165],"several":[166],"experiments":[167],"using":[168],"benchmark":[169],"datasets":[170],"assess":[172],"tolerance":[174],"verify":[176],"article.":[182],"The":[183],"comprehensive":[184],"experimental":[185],"results":[186,191],"firmly":[187],"confirm":[188],"our":[189,199],"theoretical":[190],"also":[193],"clearly":[194],"validate":[195],"effectiveness":[197],"method":[200],"various":[202],"levels":[203]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5}],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2025-10-10T00:00:00"}