{"id":"https://openalex.org/W4403791501","doi":"https://doi.org/10.1145/3664647.3680835","title":"Enhancing Robustness in Learning with Noisy Labels: An Asymmetric Co-Training Approach","display_name":"Enhancing Robustness in Learning with Noisy Labels: An Asymmetric Co-Training Approach","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791501","doi":"https://doi.org/10.1145/3664647.3680835"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680835","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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/A5074182947","display_name":"Mengmeng Sheng","orcid":"https://orcid.org/0000-0002-2011-8597"},"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":"Mengmeng Sheng","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-2011-8597","affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","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":false,"raw_author_name":"Zeren Sun","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-6262-5338","affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082756213","display_name":"Gensheng Pei","orcid":"https://orcid.org/0000-0002-7677-7487"},"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":"Gensheng Pei","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-7677-7487","affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075177714","display_name":"Tao Chen","orcid":"https://orcid.org/0000-0001-8239-1698"},"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":"Tao Chen","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-8239-1698","affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085348492","display_name":"Haonan Luo","orcid":"https://orcid.org/0000-0002-9121-2687"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haonan Luo","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9121-2687","affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","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":["Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-0337-9410","affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8328,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87843923,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4406","last_page":"4415"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9986000061035156,"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":0.9986000061035156,"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":0.9318000078201294,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9308000206947327,"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":0.7630864381790161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7307747602462769},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5573976039886475},{"id":"https://openalex.org/keywords/co-training","display_name":"Co-training","score":0.5439474582672119},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5188130736351013},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38750335574150085},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.12208330631256104},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06410908699035645}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7630864381790161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7307747602462769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5573976039886475},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.5439474582672119},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5188130736351013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38750335574150085},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.12208330631256104},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06410908699035645},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680835","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2592335154","https://openalex.org/W2964292098","https://openalex.org/W3009953871","https://openalex.org/W3034185248","https://openalex.org/W3092251340","https://openalex.org/W3093401039","https://openalex.org/W3127659116","https://openalex.org/W3138516171","https://openalex.org/W3202897408","https://openalex.org/W3203206385","https://openalex.org/W4214585162","https://openalex.org/W4226305381","https://openalex.org/W4226360734","https://openalex.org/W4295899642","https://openalex.org/W4298417552","https://openalex.org/W4313014573","https://openalex.org/W4319341372","https://openalex.org/W4362683541","https://openalex.org/W4376122697","https://openalex.org/W4377000504","https://openalex.org/W4385327947","https://openalex.org/W4385767793","https://openalex.org/W4386071515","https://openalex.org/W4386071642","https://openalex.org/W4386072209","https://openalex.org/W4386076354","https://openalex.org/W4390872979","https://openalex.org/W4392061606","https://openalex.org/W6805539215"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Label":[0],"noise,":[1],"an":[2,66,82],"inevitable":[3],"issue":[4],"in":[5,46,51,57,86,151],"various":[6],"real-world":[7,193],"datasets,":[8],"tends":[9],"to":[10,28,71,144,163,170,182],"impair":[11],"the":[12,41,73,103,111,138,153,196],"performance":[13,185],"of":[14,21,76,99,200],"deep":[15],"neural":[16],"networks.":[17],"A":[18],"large":[19],"body":[20],"literature":[22],"focuses":[23],"on":[24,121,190],"symmetric":[25,42],"co-training,":[26],"aiming":[27],"enhance":[29],"model":[30,52,89],"robustness":[31],"by":[32],"exploiting":[33],"interactions":[34],"between":[35,125,140],"models":[36],"with":[37,95],"distinct":[38],"capabilities.":[39],"However,":[40],"training":[43,84,113],"processes":[44],"employed":[45],"existing":[47],"methods":[48],"often":[49],"culminate":[50],"consensus,":[53],"diminishing":[54],"their":[55,171],"efficacy":[56],"handling":[58],"noisy":[59],"labels.":[60],"To":[61],"this":[62],"end,":[63],"we":[64,80,161],"propose":[65,116,162],"Asymmetric":[67],"Co-Training":[68],"(ACT)":[69],"method":[70,150],"mitigate":[72],"detrimental":[74],"effects":[75],"label":[77,146],"noise.":[78],"Specifically,":[79],"introduce":[81],"asymmetric":[83,128],"framework":[85],"which":[87],"one":[88],"(i.e.,":[90,105],"RTM)":[91],"is":[92,107,142],"robustly":[93],"trained":[94,109],"a":[96,134],"selected":[97],"subset":[98],"clean":[100,167],"samples":[101,168],"while":[102],"other":[104],"NTM)":[106],"conventionally":[108],"using":[110],"entire":[112],"set.":[114],"We":[115,177],"two":[117],"novel":[118],"criteria":[119],"based":[120],"agreement":[122],"and":[123,131,192,198],"discrepancy":[124],"models,":[126,141],"establishing":[127],"sample":[129,158],"selection":[130],"mining.":[132,159],"Moreover,":[133],"metric,":[135],"derived":[136],"from":[137,174],"divergence":[139],"devised":[143],"quantify":[145],"memorization,":[147],"guiding":[148],"our":[149,201],"determining":[152],"optimal":[154],"stopping":[155],"point":[156],"for":[157],"Finally,":[160],"dynamically":[164],"re-weight":[165],"identified":[166],"according":[169],"reliability":[172],"inferred":[173],"historical":[175],"information.":[176],"additionally":[178],"employ":[179],"consistency":[180],"regularization":[181],"achieve":[183],"further":[184],"improvement.":[186],"Extensive":[187],"experimental":[188],"results":[189],"synthetic":[191],"datasets":[194],"demonstrate":[195],"effectiveness":[197],"superiority":[199],"method.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}