{"id":"https://openalex.org/W4391428899","doi":"https://doi.org/10.1109/tpami.2024.3360623","title":"A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels","display_name":"A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels","publication_year":2024,"publication_date":"2024-02-01","ids":{"openalex":"https://openalex.org/W4391428899","doi":"https://doi.org/10.1109/tpami.2024.3360623","pmid":"https://pubmed.ncbi.nlm.nih.gov/38300782"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2024.3360623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3360623","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/A5101914070","display_name":"Songhua Wu","orcid":"https://orcid.org/0000-0003-2908-5530"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]},{"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","CN"],"is_corresponding":false,"raw_author_name":"Songhua Wu","raw_affiliation_strings":["Department of Automation, University of Science and Technology of China (USTC), Hefei, China","Trustworthy Machine Learning Lab, School of Computer Science, University of Sydney, Darlington, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-2908-5530","affiliations":[{"raw_affiliation_string":"Department of Automation, University of Science and Technology of China (USTC), Hefei, China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Trustworthy Machine Learning Lab, School of Computer Science, University of Sydney, Darlington, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039076312","display_name":"Tianyi Zhou","orcid":"https://orcid.org/0000-0001-5348-0632"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Zhou","raw_affiliation_strings":["School of Computer Science and UMIACS, University of Maryland, College Park, MD, USA"],"raw_orcid":"https://orcid.org/0000-0001-5348-0632","affiliations":[{"raw_affiliation_string":"School of Computer Science and UMIACS, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102777227","display_name":"Yuxuan Du","orcid":"https://orcid.org/0000-0002-1193-9756"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxuan Du","raw_affiliation_strings":["JD Explore Academy, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1193-9756","affiliations":[{"raw_affiliation_string":"JD Explore Academy, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048818071","display_name":"Jun Yu","orcid":"https://orcid.org/0000-0002-3197-8103"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Yu","raw_affiliation_strings":["Department of Automation, University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-3197-8103","affiliations":[{"raw_affiliation_string":"Department of Automation, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047240103","display_name":"Bo Han","orcid":"https://orcid.org/0000-0002-6338-0958"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Bo Han","raw_affiliation_strings":["Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong","Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-6338-0958","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong","institution_ids":["https://openalex.org/I141568987"]},{"raw_affiliation_string":"Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065250332","display_name":"Tongliang Liu","orcid":"https://orcid.org/0000-0002-9640-6472"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]},{"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","CN"],"is_corresponding":false,"raw_author_name":"Tongliang Liu","raw_affiliation_strings":["Department of Automation, University of Science and Technology of China (USTC), Hefei, China","Trustworthy Machine Learning Lab, School of Computer Science, University of Sydney, Darlington, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-9640-6472","affiliations":[{"raw_affiliation_string":"Department of Automation, University of Science and Technology of China (USTC), Hefei, China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Trustworthy Machine Learning Lab, School of Computer Science, University of Sydney, Darlington, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6731,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.90439969,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"46","issue":"7","first_page":"4830","last_page":"4842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T11220","display_name":"Water Systems and Optimization","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9879000186920166,"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":0.778113842010498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6995790004730225},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.6504455804824829},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6428074240684509},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6377083659172058},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.556525707244873},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5545352697372437},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.46952977776527405},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4553024470806122},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.42440688610076904},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3440098762512207}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.778113842010498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6995790004730225},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.6504455804824829},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6428074240684509},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6377083659172058},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.556525707244873},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5545352697372437},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46952977776527405},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4553024470806122},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.42440688610076904},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3440098762512207},{"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/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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2024.3360623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3360623","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:38300782","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38300782","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":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1070913198","display_name":null,"funder_award_id":"62076241","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1156649458","display_name":"Modelling Adversarial Noise for Trustworthy Data Analytics","funder_award_id":"FT220100318","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G2322523791","display_name":"Visual methods for advanced automation of underwater manipulation","funder_award_id":"LP220100527","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G2899355980","display_name":null,"funder_award_id":"62101371","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3548591760","display_name":"ARC Training Centre in Data Analytics for Resources and Environments (DARE)","funder_award_id":"IC190100031","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G450612882","display_name":null,"funder_award_id":"62276242","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5485303619","display_name":"Self-supervised feature learning for rapid processing of marine imagery","funder_award_id":"LP220200949","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G5944818496","display_name":"Transfer Learning Handling Causally Bilateral Shift ","funder_award_id":"DP220102121","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G6556810188","display_name":null,"funder_award_id":"62171436","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7408801154","display_name":null,"funder_award_id":"61725105","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7957291013","display_name":null,"funder_award_id":"2022A1515011652","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325902","display_name":"Beijing Municipal Science and Technology Commission","ror":null},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W1514928307","https://openalex.org/W1555168845","https://openalex.org/W1921293667","https://openalex.org/W2001610032","https://openalex.org/W2102348129","https://openalex.org/W2124219775","https://openalex.org/W2194775991","https://openalex.org/W2296073425","https://openalex.org/W2566079294","https://openalex.org/W2577784528","https://openalex.org/W2592335154","https://openalex.org/W2750384547","https://openalex.org/W2798830774","https://openalex.org/W2887842788","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2985817549","https://openalex.org/W3034185248","https://openalex.org/W3103846556","https://openalex.org/W3118608800","https://openalex.org/W3165676168","https://openalex.org/W3168393738","https://openalex.org/W3170414933","https://openalex.org/W4287252712","https://openalex.org/W6610745255","https://openalex.org/W6677082149","https://openalex.org/W6678280073","https://openalex.org/W6683557909","https://openalex.org/W6686674283","https://openalex.org/W6736021936","https://openalex.org/W6738763127","https://openalex.org/W6743688258","https://openalex.org/W6744106388","https://openalex.org/W6745136726","https://openalex.org/W6750523955","https://openalex.org/W6750701765","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6751661576","https://openalex.org/W6751883947","https://openalex.org/W6753772092","https://openalex.org/W6763576130","https://openalex.org/W6767203202","https://openalex.org/W6768332837","https://openalex.org/W6768967079","https://openalex.org/W6771630921","https://openalex.org/W6772376969","https://openalex.org/W6772389601","https://openalex.org/W6779708913","https://openalex.org/W6779784029","https://openalex.org/W6781063151","https://openalex.org/W6783943466","https://openalex.org/W6784235280","https://openalex.org/W6785488711","https://openalex.org/W6785494106","https://openalex.org/W6785557519","https://openalex.org/W6785714404","https://openalex.org/W6785821353","https://openalex.org/W6787972765","https://openalex.org/W6789948128","https://openalex.org/W6790117478","https://openalex.org/W6790968595","https://openalex.org/W6793012311","https://openalex.org/W6796290586","https://openalex.org/W6796408964","https://openalex.org/W6796912519","https://openalex.org/W6801205975","https://openalex.org/W6803226316"],"related_works":["https://openalex.org/W2044488462","https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W2163707935","https://openalex.org/W83146503","https://openalex.org/W202723009","https://openalex.org/W2770533869","https://openalex.org/W4206462905","https://openalex.org/W2165396616"],"abstract_inverted_index":{"Many":[0],"machine":[1],"learning":[2,96,208],"algorithms":[3],"are":[4,20,25],"known":[5],"to":[6,85,116,126,169],"be":[7],"fragile":[8],"on":[9,134,137,213],"simple":[10],"instance-independent":[11],"noisy":[12,15,72,102,182,211],"labels.":[13,103],"However,":[14],"labels":[16,156,183,212],"in":[17,31,176,207],"real-world":[18],"data":[19,113,132,152],"more":[21,28],"devastating":[22],"since":[23],"they":[24],"produced":[26],"by":[27,47],"complicated":[29],"mechanisms":[30],"an":[32,61],"instance-dependent":[33,62,181,210],"manner.":[34],"In":[35],"this":[36,40],"paper,":[37],"we":[38],"target":[39],"practical":[41],"challenge":[42],"of":[43,97,112,119,145,180,203],"Instance-Dependent":[44],"Noisy":[45],"Labels":[46],"jointly":[48],"training":[49,120,131,162],"(1)":[50],"a":[51,76,128],"model":[52,90],"reversely":[53],"engineering":[54],"the":[55,65,70,88,98,110,117,138,143,150,163,199,204],"noise":[56],"generating":[57],"mechanism,":[58],"which":[59],"produces":[60,80],"mapping":[63],"between":[64],"clean":[66,81,155],"label":[67,73,82],"posterior":[68],"and":[69,74,93,123,157,171,194,201],"observed":[71],"(2)":[75],"robust":[77,172],"classifier":[78],"that":[79,109,149],"posteriors.":[83],"Compared":[84],"previous":[86],"methods,":[87],"former":[89],"is":[91,114],"novel":[92],"enables":[94],"end-to-end":[95],"latter":[99],"directly":[100],"from":[101,209],"An":[104],"extensive":[105],"empirical":[106],"study":[107],"indicates":[108],"time-consistency":[111,205],"critical":[115],"success":[118],"both":[121,154],"models":[122],"motivates":[124],"us":[125],"develop":[127],"curriculum":[129,206],"selecting":[130],"based":[133],"their":[135],"dynamics":[136],"two":[139,164],"models'":[140],"outputs":[141],"over":[142],"course":[144],"training.":[146],"We":[147],"show":[148],"curriculum-selected":[151],"provide":[153],"high-quality":[158],"input-output":[159],"pairs":[160],"for":[161],"models.":[165],"Therefore,":[166],"it":[167],"leads":[168],"promising":[170],"classification":[173],"performance":[174],"even":[175],"notably":[177],"challenging":[178],"settings":[179],"where":[184],"many":[185],"SoTA":[186],"methods":[187],"could":[188],"easily":[189],"fail.":[190],"Extensive":[191],"experimental":[192],"comparisons":[193],"ablation":[195],"studies":[196],"further":[197],"demonstrate":[198],"advantages":[200],"significance":[202],"multiple":[214],"benchmark":[215],"datasets.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}