{"id":"https://openalex.org/W4312506025","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892927","title":"Learning Multi-Level Consistency for Noisy Labels","display_name":"Learning Multi-Level Consistency for Noisy Labels","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312506025","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892927"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892927","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 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/A5019637260","display_name":"Ziye Tong","orcid":"https://orcid.org/0000-0002-2910-196X"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziye Tong","raw_affiliation_strings":["School of Computer Science&Technology, Xi'an Jiaotong University,Xi'an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science&Technology, Xi'an Jiaotong University,Xi'an,China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050052141","display_name":"Wei Xi","orcid":"https://orcid.org/0000-0001-9348-2982"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Xi","raw_affiliation_strings":["School of Computer Science&Technology, Xi'an Jiaotong University,Xi'an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science&Technology, Xi'an Jiaotong University,Xi'an,China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"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":"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9684000015258789,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9666000008583069,"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":0.7742523550987244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5970598459243774},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5784581899642944},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.503884494304657},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4966660141944885},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.49327677488327026},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.48368507623672485},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4607348144054413},{"id":"https://openalex.org/keywords/data-consistency","display_name":"Data consistency","score":0.45763471722602844},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4296766221523285},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4264554977416992},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3572591245174408},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.22703993320465088}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7742523550987244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5970598459243774},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5784581899642944},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.503884494304657},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4966660141944885},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.49327677488327026},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.48368507623672485},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4607348144054413},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.45763471722602844},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4296766221523285},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4264554977416992},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3572591245174408},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.22703993320465088},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892927","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 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","score":0.550000011920929,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G8458085269","display_name":null,"funder_award_id":"61832008,62176205","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1921293667","https://openalex.org/W2121056381","https://openalex.org/W2124219775","https://openalex.org/W2183341477","https://openalex.org/W2592335154","https://openalex.org/W2765407302","https://openalex.org/W2795282075","https://openalex.org/W2803187616","https://openalex.org/W2804378530","https://openalex.org/W2945634539","https://openalex.org/W2947380870","https://openalex.org/W2948606739","https://openalex.org/W2949736877","https://openalex.org/W2951292523","https://openalex.org/W2952361104","https://openalex.org/W2953070460","https://openalex.org/W2963160702","https://openalex.org/W2963509340","https://openalex.org/W2963735582","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2978625989","https://openalex.org/W2979302305","https://openalex.org/W2981873476","https://openalex.org/W2991599149","https://openalex.org/W2996108195","https://openalex.org/W3035014997","https://openalex.org/W3039366696","https://openalex.org/W3118608800","https://openalex.org/W3137695714","https://openalex.org/W3175103763","https://openalex.org/W4287812705","https://openalex.org/W4288020585","https://openalex.org/W6640298173","https://openalex.org/W6678280073","https://openalex.org/W6733814495","https://openalex.org/W6745136726","https://openalex.org/W6745523664","https://openalex.org/W6750523955","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6751883947","https://openalex.org/W6758632346","https://openalex.org/W6762161020","https://openalex.org/W6762903502","https://openalex.org/W6763430915","https://openalex.org/W6763485134","https://openalex.org/W6764266060","https://openalex.org/W6770366593","https://openalex.org/W6770578729","https://openalex.org/W6771630921","https://openalex.org/W6776700526","https://openalex.org/W6781063151"],"related_works":["https://openalex.org/W3082232744","https://openalex.org/W4313561330","https://openalex.org/W4388820213","https://openalex.org/W2395693197","https://openalex.org/W2962369866","https://openalex.org/W1575623062","https://openalex.org/W3172741267","https://openalex.org/W4390341236","https://openalex.org/W3035700741","https://openalex.org/W1522341235"],"abstract_inverted_index":{"Recent":[0],"methods":[1,35],"performing":[2],"well":[3],"on":[4,14,138],"Learning":[5],"with":[6,38,144],"Noisy":[7],"Label":[8],"(LNL)":[9],"problem":[10,46],"generally":[11],"are":[12],"based":[13],"semi-supervised":[15,31],"learning":[16],"and":[17,30,47,58,64,87,106],"consistency":[18,40,89],"regularization.":[19],"It":[20],"usually":[21],"consists":[22],"of":[23,67,122,154],"three":[24],"stages:":[25],"warm-up,":[26],"noisy/clean":[27],"data":[28,105,124,128],"division,":[29],"learning.":[32],"However,":[33],"these":[34],"trained":[36],"purely":[37],"classification":[39,88],"suffer":[41],"from":[42],"the":[43,51,62,68,100,120,135],"confirmation":[44],"bias":[45],"tend":[48],"to":[49,98],"memorize":[50],"noisy":[52,69,107,127,146],"labels,":[53,147],"resulting":[54],"in":[55],"accumulated":[56],"error":[57],"degraded":[59],"performance.":[60],"Leveraging":[61],"compositional":[63],"relational":[65],"peculiarities":[66],"data,":[70],"we":[71,110],"propose":[72,111],"a":[73,112,151],"graph-based":[74],"Multi-Level":[75],"Consistency":[76],"(MLC)":[77],"framework":[78],"that":[79],"jointly":[80],"exploits":[81],"multi-level":[82,101],"relation":[83],"consistencies":[84],"between":[85,103],"graphs":[86],"which":[90,117],"can":[91],"better":[92],"correct":[93],"wrong":[94],"labels":[95],"by":[96,125,158],"continuing":[97],"learn":[99],"differences":[102],"clean":[104],"data.":[108],"Moreover,":[109],"Dynamic":[113],"Filter":[114],"Module":[115],"(DFM)":[116],"effectively":[118],"improves":[119],"reliability":[121],"divided":[123],"re-filtering":[126],"despite":[129],"its":[130],"simplicity.":[131],"Our":[132],"method":[133,149],"achieves":[134,150],"state-of-the-art":[136],"performance":[137],"multiple":[139],"benchmark":[140],"datasets.":[141],"On":[142],"Cifar-100":[143],"90%":[145],"our":[148],"top-1":[152],"accuracy":[153],"49.1%,":[155],"outperforming":[156],"DivideMix":[157],"17.6%.":[159]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}