{"id":"https://openalex.org/W4225310585","doi":"https://doi.org/10.1109/icassp43922.2022.9747279","title":"OT Cleaner: Label Correction as Optimal Transport","display_name":"OT Cleaner: Label Correction as Optimal Transport","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4225310585","doi":"https://doi.org/10.1109/icassp43922.2022.9747279"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9747279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747279","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 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/A5087672753","display_name":"Jun Xia","orcid":"https://orcid.org/0000-0002-7993-0803"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Xia","raw_affiliation_strings":["Zhejiang University,Hangzhou,China,310058","AI Lab, School of Engineering, Westlake University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University,Hangzhou,China,310058","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"AI Lab, School of Engineering, Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006542157","display_name":"Cheng Tan","orcid":"https://orcid.org/0000-0002-8639-923X"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Tan","raw_affiliation_strings":["Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041519364","display_name":"Lirong Wu","orcid":"https://orcid.org/0000-0001-5551-3194"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lirong Wu","raw_affiliation_strings":["Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042875688","display_name":"Yongjie Xu","orcid":"https://orcid.org/0000-0002-2660-6575"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjie Xu","raw_affiliation_strings":["Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082786719","display_name":"Stan Z. Li","orcid":"https://orcid.org/0000-0002-2961-8096"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Stan Z. Li","raw_affiliation_strings":["Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University,School of Engineering,AI Lab,Hangzhou,China,310024","institution_ids":["https://openalex.org/I3133055985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8556,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.87414736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3953","last_page":"3957"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9886999726295471,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9811000227928162,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7772338390350342},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7474570274353027},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6335703730583191},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6142403483390808},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6107473373413086},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5519480109214783},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5391900539398193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5299925208091736},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5179149508476257},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.5128455758094788},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42768219113349915},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.427432656288147},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3374261260032654},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3329654335975647},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32438021898269653},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11344796419143677}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7772338390350342},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7474570274353027},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6335703730583191},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6142403483390808},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6107473373413086},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5519480109214783},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5391900539398193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5299925208091736},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5179149508476257},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.5128455758094788},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42768219113349915},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.427432656288147},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3374261260032654},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3329654335975647},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32438021898269653},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11344796419143677},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9747279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747279","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5299999713897705}],"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":35,"referenced_works":["https://openalex.org/W385466589","https://openalex.org/W1514928307","https://openalex.org/W1921293667","https://openalex.org/W2003447360","https://openalex.org/W2121056381","https://openalex.org/W2158131535","https://openalex.org/W2539033431","https://openalex.org/W2743200750","https://openalex.org/W2804077623","https://openalex.org/W2945007112","https://openalex.org/W2952361104","https://openalex.org/W2963096987","https://openalex.org/W2963703197","https://openalex.org/W2963735582","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2996108195","https://openalex.org/W3034185248","https://openalex.org/W3034266240","https://openalex.org/W3121904315","https://openalex.org/W3137695714","https://openalex.org/W6640298173","https://openalex.org/W6678280073","https://openalex.org/W6682962330","https://openalex.org/W6740005241","https://openalex.org/W6742511895","https://openalex.org/W6751037545","https://openalex.org/W6751647823","https://openalex.org/W6751661576","https://openalex.org/W6762161020","https://openalex.org/W6762892961","https://openalex.org/W6763485134","https://openalex.org/W6771630921","https://openalex.org/W6779708913","https://openalex.org/W6788752698"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2239445980","https://openalex.org/W2080152487","https://openalex.org/W3083152911","https://openalex.org/W4220659530","https://openalex.org/W3000197790"],"abstract_inverted_index":{"Datasets":[0],"with":[1,12,36,41,100],"noisy":[2,23],"labels":[3,24],"present":[4],"challenges":[5],"for":[6,25,88],"training":[7,119],"Deep":[8],"Neural":[9],"Networks":[10],"(DNNs)":[11],"high":[13],"generalization":[14],"ability.":[15],"An":[16],"direct":[17],"idea":[18],"is":[19,126],"to":[20,59,71,79],"correct":[21],"the":[22,50,68,72,85,109],"robust":[26],"learning.":[27],"However,":[28],"existing":[29],"label":[30,56,69,105],"correction":[31,70],"methods":[32],"can":[33],"not":[34],"handle":[35],"heavy":[37],"noise":[38,106],"or":[39],"datasets":[40,99],"samples":[42],"of":[43,84,111,117],"many":[44],"categories":[45],"so":[46],"well.":[47],"We":[48],"explain":[49],"reasons":[51],"and":[52,77,103,121],"introduce":[53],"a":[54,81],"global":[55],"distribution":[57],"regularization":[58],"remedy":[60],"these":[61],"deficiencies.":[62],"With":[63],"this":[64],"regularization,":[65],"we":[66],"convert":[67],"Optimal":[73],"Transport":[74],"(OT)":[75],"formulation":[76],"propose":[78],"utilize":[80],"fast":[82],"version":[83],"Sinkhorn-Knopp":[86],"algorithm":[87],"finding":[89],"an":[90],"approximate":[91],"solution":[92],"efficiently":[93],"at":[94],"scale.":[95],"Experiments":[96],"on":[97],"benchmark":[98],"both":[101,118],"synthetic":[102],"real-world":[104],"show":[107],"that":[108],"superiority":[110],"our":[112],"OT":[113],"Cleaner":[114],"in":[115],"terms":[116],"efficiency":[120],"classification":[122],"accuracy.":[123],"The":[124],"code":[125],"available":[127],"at:":[128],"https://github.com/junxia97/OT-Cleaner.":[129]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
