{"id":"https://openalex.org/W7164356115","doi":"https://doi.org/10.48550/arxiv.2606.11695","title":"Noise-Aware Framework for Correcting Corrupted Labels","display_name":"Noise-Aware Framework for Correcting Corrupted Labels","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164356115","doi":"https://doi.org/10.48550/arxiv.2606.11695"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.11695","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003899792","display_name":"Ha-Linh Nguyen","orcid":"https://orcid.org/0000-0002-2538-7657"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Ha-Linh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138432922","display_name":"Hong-Anh Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Hong-Anh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138415495","display_name":"Minh-Duc La","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"La, Minh-Duc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130821316","display_name":"Phong Lam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lam, Phong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138445581","display_name":"Thu-Trang Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Thu-Trang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138451623","display_name":"Son Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Son","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084251723","display_name":"Hieu Dinh Vo","orcid":"https://orcid.org/0000-0002-9407-1971"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vo, Hieu Dinh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.972599983215332,"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":0.972599983215332,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.005799999926239252,"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/T10057","display_name":"Face and Expression Recognition","score":0.0012000000569969416,"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/robustness","display_name":"Robustness (evolution)","score":0.6859999895095825},{"id":"https://openalex.org/keywords/canola","display_name":"Canola","score":0.6754000186920166},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5551999807357788},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5547000169754028},{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.44200000166893005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4325000047683716},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4316999912261963}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7260000109672546},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6859999895095825},{"id":"https://openalex.org/C2779223168","wikidata":"https://www.wikidata.org/wiki/Q98841400","display_name":"Canola","level":2,"score":0.6754000186920166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6424000263214111},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5551999807357788},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5547000169754028},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49000000953674316},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4316999912261963},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.367000013589859},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34869998693466187},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.32839998602867126},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.2621999979019165}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.11695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-quality":[0],"labeled":[1],"data":[2,191],"is":[3,98],"essential":[4],"for":[5,37],"training":[6,64],"reliable":[7],"ML/DL":[8],"models.":[9],"However,":[10],"real-world":[11],"datasets":[12,143,175],"often":[13],"contain":[14],"a":[15,34,66,131],"considerable":[16],"proportion":[17],"of":[18,55,65,199],"corrupted":[19,39],"labels,":[20],"which":[21,107],"can":[22,192],"severely":[23],"degrade":[24],"model":[25,80,108],"performance.":[26],"To":[27],"address":[28],"this":[29,60],"problem,":[30],"we":[31],"propose":[32],"CANOLA,":[33],"novel":[35],"framework":[36],"correcting":[38],"labels":[40,114],"through":[41],"noise-aware":[42,67],"learning":[43],"and":[44,58,86,94,133],"iterative":[45,102],"label":[46,104,157],"refinement.":[47],"CANOLA":[48,77,138,153,178],"explicitly":[49],"estimates":[50],"the":[51,56,63,79,125],"underlying":[52],"noise":[53,73],"distribution":[54],"dataset":[57,126],"incorporates":[59],"information":[61],"into":[62],"Deep":[68],"Neural":[69],"Network.":[70],"By":[71],"incorporating":[72],"characteristics":[74],"during":[75],"learning,":[76],"enables":[78],"to":[81,115,127,166,201],"down-weight":[82],"unreliable":[83],"supervision":[84],"signals":[85],"focus":[87],"on":[88,139,174,188],"trustworthy":[89],"patterns,":[90],"thereby":[91],"improving":[92],"robustness":[93],"generalization.":[95],"Label":[96],"correction":[97,158],"performed":[99],"via":[100],"cautious,":[101],"soft":[103],"refinement,":[105],"in":[106,130,168],"predictions":[109],"are":[110],"blended":[111],"with":[112],"observed":[113],"prevent":[116],"premature":[117],"or":[118],"erroneous":[119],"updates.":[120],"This":[121],"progressive":[122],"refinement":[123],"allows":[124],"be":[128],"repaired":[129],"stable":[132],"controlled":[134],"manner.":[135],"We":[136],"evaluate":[137],"six":[140],"widely":[141],"used":[142],"under":[144],"realistic":[145],"noisy":[146],"labeling":[147],"scenarios.":[148],"Experimental":[149],"results":[150],"show":[151],"that":[152],"consistently":[154],"outperforms":[155],"SOTA":[156],"methods,":[159],"achieving":[160],"relative":[161],"improvements":[162],"ranging":[163],"from":[164],"19%":[165],"52%":[167],"error":[169],"reduction.":[170],"Moreover,":[171],"models":[172],"trained":[173,187],"corrected":[176,190],"by":[177,197],"obtain":[179],"substantial":[180],"downstream":[181],"performance":[182],"gains.":[183],"Even":[184],"simple":[185],"classifiers":[186],"CANOLA's":[189],"outperform":[193],"complex":[194],"model-centric":[195],"approaches":[196],"margins":[198],"up":[200],"67%.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-12T00:00:00"}
