{"id":"https://openalex.org/W4408345900","doi":"https://doi.org/10.1109/icassp49660.2025.10889183","title":"Sufficient Learning for Label Noise with Dual-Regularization","display_name":"Sufficient Learning for Label Noise with Dual-Regularization","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408345900","doi":"https://doi.org/10.1109/icassp49660.2025.10889183"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5075546663","display_name":"Yongfeng Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongfeng Dong","raw_affiliation_strings":["Hebei University of Technology,School of Artificial Intelligence,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei University of Technology,School of Artificial Intelligence,Tianjin,China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102346650","display_name":"Guifang Wu","orcid":"https://orcid.org/0009-0002-2169-6054"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guifang Wu","raw_affiliation_strings":["Hebei University of Technology,School of Artificial Intelligence,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei University of Technology,School of Artificial Intelligence,Tianjin,China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041076952","display_name":"Jiaji Wang","orcid":"https://orcid.org/0000-0002-6728-2685"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaji Wang","raw_affiliation_strings":["Hebei University of Technology,School of Artificial Intelligence,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei University of Technology,School of Artificial Intelligence,Tianjin,China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100422430","display_name":"Zhen Wang","orcid":"https://orcid.org/0000-0003-3907-4595"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Wang","raw_affiliation_strings":["Hebei University of Technology,School of Artificial Intelligence,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei University of Technology,School of Artificial Intelligence,Tianjin,China","institution_ids":["https://openalex.org/I184843921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I184843921"],"apc_list":null,"apc_paid":null,"fwci":3.346,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.90274141,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.917900025844574,"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/T10320","display_name":"Neural Networks and Applications","score":0.917900025844574,"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":0.6221457123756409},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.5822433233261108},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5775230526924133},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44298309087753296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4055078327655792},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3439943790435791}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6221457123756409},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.5822433233261108},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5775230526924133},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44298309087753296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4055078327655792},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3439943790435791},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"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":23,"referenced_works":["https://openalex.org/W2566079294","https://openalex.org/W2963697299","https://openalex.org/W3034185248","https://openalex.org/W3121661476","https://openalex.org/W3135179859","https://openalex.org/W3153990350","https://openalex.org/W3183932535","https://openalex.org/W3190486510","https://openalex.org/W3202897408","https://openalex.org/W4206722749","https://openalex.org/W4287947470","https://openalex.org/W4312421335","https://openalex.org/W4312815685","https://openalex.org/W4319299742","https://openalex.org/W4376143078","https://openalex.org/W4385568120","https://openalex.org/W4386113250","https://openalex.org/W4390872979","https://openalex.org/W6758632346","https://openalex.org/W6762913911","https://openalex.org/W6771630921","https://openalex.org/W6779304510","https://openalex.org/W6801933390"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W2952466936","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W4236344264","https://openalex.org/W3046201198","https://openalex.org/W4293061921","https://openalex.org/W2807441102","https://openalex.org/W2009424630","https://openalex.org/W2372693287"],"abstract_inverted_index":{"DNNs":[0],"inevitably":[1],"over-fits":[2],"to":[3,22,84,97,111],"label":[4,16],"noisy":[5,54,77,119,128,134,138],"sample,":[6],"resulting":[7],"in":[8,52,89,105,118],"poor":[9],"generalization.":[10],"To":[11,56],"mitigate":[12],"the":[13,28,36,41,49,53,81,86,113,143],"influence":[14],"of":[15,43,142],"noise,":[17],"Co-teaching":[18],"based":[19,31,79],"methods":[20],"tend":[21],"select":[23],"potentially":[24],"clean":[25,44,75,106],"data":[26],"as":[27],"training":[29,72],"samples":[30,73,107],"on":[32,80,123],"small-loss":[33,37,82],"criterion.":[34],"However,":[35],"criterion":[38,83],"cannot":[39],"guarantee":[40],"purity":[42],"samples,":[45],"and":[46,76,101,108,132,136],"blithely":[47],"ignores":[48],"information":[50],"involved":[51],"samples.":[55,120],"address":[57],"this":[58],"issue,":[59],"we":[60,70,93],"propose":[61],"a":[62],"simple":[63],"yet":[64],"effective":[65],"method":[66],"called":[67],"SLDR.":[68],"Specifically,":[69],"partition":[71],"into":[74],"subsets":[78],"filter":[85],"errors":[87],"generated":[88],"each":[90],"network.":[91],"Then,":[92],"employ":[94],"supervised":[95],"regularization":[96,110],"minimize":[98],"intra-class":[99],"distances":[100,104],"maximize":[102],"inter-class":[103],"unsupervised":[109],"assess":[112],"similarity":[114],"between":[115],"instance":[116],"features":[117],"Extensive":[121],"experiments":[122],"mainstream":[124],"benchmarks,":[125],"including":[126],"synthetic":[127],"datasets":[129,139],"(noisy":[130],"version":[131],"long-tailed":[133],"version),":[135],"real-world":[137],"demonstrate":[140],"superiority":[141],"proposed":[144],"method.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}