{"id":"https://openalex.org/W4402351251","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650630","title":"Progressively Robust Loss for Deep Learning with Noisy Labels","display_name":"Progressively Robust Loss for Deep Learning with Noisy Labels","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351251","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650630"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650630","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650630","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/A5023809922","display_name":"Zhenhuang Cai","orcid":"https://orcid.org/0000-0002-4774-9073"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhuang Cai","raw_affiliation_strings":["Hohai University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University,Nanjing,China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101706784","display_name":"Shuai Yan","orcid":"https://orcid.org/0000-0002-0913-342X"},"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":"Shuai Yan","raw_affiliation_strings":["Nanjing University of Science and Technology,Nanjing,China"],"raw_orcid":null,"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/A5101504770","display_name":"Yuanbo Chen","orcid":"https://orcid.org/0000-0002-7006-2229"},"institutions":[{"id":"https://openalex.org/I4210117825","display_name":"Beijing Research Institute of Mechanical and Electrical Technology","ror":"https://ror.org/02bjnsn63","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210117825"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanbo Chen","raw_affiliation_strings":["Beijing Research Institute of Mechanical and Electrical Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Research Institute of Mechanical and Electrical Engineering,Beijing,China","institution_ids":["https://openalex.org/I4210117825"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101465467","display_name":"Chuanyi Zhang","orcid":"https://orcid.org/0000-0001-8724-5796"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanyi Zhang","raw_affiliation_strings":["Hohai University,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University,Nanjing,China","institution_ids":["https://openalex.org/I163340411"]}]},{"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":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]},{"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":null,"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":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"1","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":0.9994999766349792,"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":0.9994999766349792,"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":0.9883999824523926,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7040691375732422},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5445398092269897},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5264785289764404},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4192754626274109}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7040691375732422},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5445398092269897},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5264785289764404},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4192754626274109},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650630","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650630","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":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320330944","display_name":"Nature","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1921293667","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2566079294","https://openalex.org/W2743200750","https://openalex.org/W2884115684","https://openalex.org/W2963697299","https://openalex.org/W2964155802","https://openalex.org/W2964292098","https://openalex.org/W2981873476","https://openalex.org/W2999199619","https://openalex.org/W3034185248","https://openalex.org/W3035546924","https://openalex.org/W3049063470","https://openalex.org/W3092251340","https://openalex.org/W3109225549","https://openalex.org/W3173874704","https://openalex.org/W3193137605","https://openalex.org/W3211938211","https://openalex.org/W4285232958","https://openalex.org/W4312601326","https://openalex.org/W4319341372","https://openalex.org/W6726497184","https://openalex.org/W6740005241","https://openalex.org/W6742511895","https://openalex.org/W6745891213","https://openalex.org/W6747898760","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6762161020","https://openalex.org/W6762892961","https://openalex.org/W6768967079","https://openalex.org/W6771630921","https://openalex.org/W6771936042","https://openalex.org/W6772653547","https://openalex.org/W6774097037","https://openalex.org/W6779784029","https://openalex.org/W6787972765","https://openalex.org/W6789476682","https://openalex.org/W6795484856","https://openalex.org/W6796912519","https://openalex.org/W6797999826","https://openalex.org/W6810976116"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"Learning":[0],"with":[1,97,152],"noisy":[2,27,67,134,153],"labels":[3,28],"(LNL)":[4],"plays":[5],"a":[6,74],"pivotal":[7],"role":[8],"in":[9,95,150],"arming":[10],"deep":[11,99],"neural":[12],"networks":[13],"(DNNs)":[14],"to":[15,23,42,54,62,82,91],"combat":[16],"label":[17],"noise.":[18],"Early":[19],"noise-robust":[20,86],"functions":[21],"tend":[22],"promote":[24],"robustness":[25],"against":[26],"at":[29,159],"the":[30,63,98,108,116,140],"cost":[31],"of":[32,50,65,107,111,119,142],"sacrificing":[33],"data-fitting":[34],"ability.":[35],"Recent":[36],"robust":[37,56,79,147],"loss":[38,80,148],"methods":[39,149],"typically":[40],"try":[41],"balance":[43],"noise-robustness":[44,118],"and":[45,129,131,137],"learning":[46],"capability.":[47],"However,":[48],"most":[49],"them":[51],"generally":[52],"descend":[53],"partially":[55],"losses,":[57],"which":[58,93],"are":[59],"still":[60],"exposed":[61],"risk":[64,113],"overfitting":[66],"labels.":[68,154],"To":[69],"this":[70],"end,":[71],"we":[72],"propose":[73],"novel":[75],"paradigm":[76],"named":[77],"progressively":[78],"framework":[81],"dynamically":[83],"guide":[84],"existing":[85],"losses":[87],"from":[88],"fast":[89],"convergence":[90],"noise-tolerant,":[92],"is":[94,157],"accord":[96],"models\u2019":[100],"memorization":[101],"effect.":[102],"Furthermore,":[103],"our":[104,120,143],"theoretical":[105],"analysis":[106],"upper":[109],"bounds":[110],"empirical":[112],"errors":[114],"illustrates":[115],"increasing":[117],"approach.":[121],"Experimental":[122],"results":[123],"on":[124],"two":[125,132],"synthetic":[126],"benchmarks":[127],"(CIFAR-100N":[128],"CIFAR-80N)":[130],"real-world":[133],"datasets":[135],"(WebFG-496":[136],"Webvision)":[138],"demonstrate":[139],"superiority":[141],"approach":[144],"over":[145],"state-of-the-art":[146],"dealing":[151],"The":[155],"code":[156],"available":[158],"https://github.com/ptcepgce/ptcepgce.":[160]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
