{"id":"https://openalex.org/W4402352736","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651351","title":"Learning with Noisy Labels: A Novel Meta Label Corrector without Clean Validation Data","display_name":"Learning with Noisy Labels: A Novel Meta Label Corrector without Clean Validation Data","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352736","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651351"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651351","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5010999540","display_name":"Kai Jiang","orcid":"https://orcid.org/0000-0002-5706-7834"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Jiang","raw_affiliation_strings":["Zhejiang University of Technology,Department of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University of Technology,Department of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101755033","display_name":"Bin Cao","orcid":"https://orcid.org/0000-0003-1062-6309"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Cao","raw_affiliation_strings":["Zhejiang University of Technology,Department of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University of Technology,Department of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100400515","display_name":"Yuqi Liu","orcid":"https://orcid.org/0000-0002-6699-5982"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqi Liu","raw_affiliation_strings":["Zhejiang University of Technology,Department of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University of Technology,Department of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030881288","display_name":"Jing Fan","orcid":"https://orcid.org/0000-0001-8777-4604"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Fan","raw_affiliation_strings":["Zhejiang University of Technology,Department of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University of Technology,Department of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I55712492"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I55712492"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"1","last_page":"8"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9427000284194946,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9301999807357788,"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":0.6750585436820984},{"id":"https://openalex.org/keywords/predictor\u2013corrector-method","display_name":"Predictor\u2013corrector method","score":0.47870659828186035},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4154694080352783},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3679639995098114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3622286319732666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34566137194633484},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21697276830673218}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6750585436820984},{"id":"https://openalex.org/C157977623","wikidata":"https://www.wikidata.org/wiki/Q2650267","display_name":"Predictor\u2013corrector method","level":2,"score":0.47870659828186035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4154694080352783},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3679639995098114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3622286319732666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34566137194633484},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21697276830673218}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651351","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5}],"awards":[],"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":56,"referenced_works":["https://openalex.org/W1972675781","https://openalex.org/W2059471177","https://openalex.org/W2065151783","https://openalex.org/W2073389244","https://openalex.org/W2123958887","https://openalex.org/W2296073425","https://openalex.org/W2749928749","https://openalex.org/W2896457183","https://openalex.org/W2947049994","https://openalex.org/W2947380870","https://openalex.org/W2951013084","https://openalex.org/W2951104886","https://openalex.org/W2963659419","https://openalex.org/W2963735582","https://openalex.org/W2963909971","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2964309657","https://openalex.org/W2978426779","https://openalex.org/W2991349609","https://openalex.org/W2996108195","https://openalex.org/W3035325670","https://openalex.org/W3035716296","https://openalex.org/W3046688540","https://openalex.org/W3110687497","https://openalex.org/W3136021864","https://openalex.org/W3139144418","https://openalex.org/W3156669901","https://openalex.org/W4288095202","https://openalex.org/W4289293816","https://openalex.org/W4301183982","https://openalex.org/W4307291644","https://openalex.org/W4307823382","https://openalex.org/W4313014573","https://openalex.org/W6683107984","https://openalex.org/W6740005241","https://openalex.org/W6743802245","https://openalex.org/W6747898760","https://openalex.org/W6750523955","https://openalex.org/W6751647823","https://openalex.org/W6752515464","https://openalex.org/W6753772092","https://openalex.org/W6755207826","https://openalex.org/W6756455746","https://openalex.org/W6757248479","https://openalex.org/W6760993166","https://openalex.org/W6762161020","https://openalex.org/W6762913911","https://openalex.org/W6763430915","https://openalex.org/W6763576130","https://openalex.org/W6768848008","https://openalex.org/W6770362605","https://openalex.org/W6771630921","https://openalex.org/W6773116539","https://openalex.org/W6774796142","https://openalex.org/W6779482673"],"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/W2033914206"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"are":[3],"susceptible":[4],"to":[5,41,57,78],"overfitting":[6],"with":[7,15,97,118],"noisy":[8,16,99],"labels.":[9],"Existing":[10],"meta-learning-based":[11],"methods":[12,35],"for":[13],"learning":[14,101],"labels":[17],"require":[18],"additional":[19],"clean":[20,82],"validation":[21,83],"data,":[22],"resulting":[23],"in":[24,111],"high":[25],"annotation":[26],"costs":[27],"and":[28,85,115,123],"limiting":[29],"their":[30],"general":[31],"applicability.":[32],"Moreover,":[33],"these":[34],"only":[36],"use":[37],"local":[38],"gradient":[39],"information":[40],"approximately":[42],"optimize":[43],"the":[44,59,75,106],"model":[45],"parameters.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50],"propose":[51],"a":[52,80,90],"novel":[53],"meta":[54],"label":[55,100,120],"corrector":[56],"address":[58],"above":[60],"two":[61,68],"issues.":[62],"Our":[63],"method":[64,110],"is":[65],"based":[66],"on":[67],"core":[69],"points:":[70],"monitoring":[71],"training":[72],"dynamics":[73],"through":[74,89],"meta-entropy":[76],"value":[77],"construct":[79],"proxy":[81],"dataset,":[84],"precise":[86],"parameter":[87],"optimization":[88,94],"new":[91],"combined":[92],"bi-level":[93],"mechanism.":[95],"Compared":[96],"state-of-the-art":[98],"methods,":[102],"comprehensive":[103],"experiments":[104],"confirm":[105],"superiority":[107],"of":[108],"our":[109],"both":[112],"image":[113],"recognition":[114],"text":[116],"classification":[117],"different":[119],"noise":[121],"types":[122],"ratios.":[124]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}