{"id":"https://openalex.org/W3201209187","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533562","title":"LABELNET: Recovering Noisy Labels","display_name":"LABELNET: Recovering Noisy Labels","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201209187","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533562","mag":"3201209187"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5029394537","display_name":"Amirmasoud Ghiassi","orcid":null},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Amirmasoud Ghiassi","raw_affiliation_strings":["TU Delft, Delft, Netherlands"],"affiliations":[{"raw_affiliation_string":"TU Delft, Delft, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000838509","display_name":"Robert Birke","orcid":"https://orcid.org/0000-0003-1144-3707"},"institutions":[{"id":"https://openalex.org/I885143765","display_name":"ABB (Switzerland)","ror":"https://ror.org/00ks5vt51","country_code":"CH","type":"company","lineage":["https://openalex.org/I885143765"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Robert Birke","raw_affiliation_strings":["ABB Corporate Research, Baden-D\u00e4ttwil, Switzerland"],"affiliations":[{"raw_affiliation_string":"ABB Corporate Research, Baden-D\u00e4ttwil, Switzerland","institution_ids":["https://openalex.org/I885143765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012832003","display_name":"Rui Han","orcid":"https://orcid.org/0000-0001-6894-1921"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Han","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013859152","display_name":"Lydia Y. Chen","orcid":"https://orcid.org/0000-0002-4228-6735"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Lydia Y. Chen","raw_affiliation_strings":["TU Delft, Delft, Netherlands"],"affiliations":[{"raw_affiliation_string":"TU Delft, Delft, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5029394537"],"corresponding_institution_ids":["https://openalex.org/I98358874"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13371563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9904000163078308,"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"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","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"}}],"keywords":[{"id":"https://openalex.org/keywords/amateur","display_name":"Amateur","score":0.8373364210128784},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7835779786109924},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7138523459434509},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6271048188209534},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6149927973747253},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.614234209060669},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5931041836738586},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5906076431274414},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5513854622840881},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49397116899490356},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.48242148756980896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3397335112094879}],"concepts":[{"id":"https://openalex.org/C2778044066","wikidata":"https://www.wikidata.org/wiki/Q455595","display_name":"Amateur","level":2,"score":0.8373364210128784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7835779786109924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7138523459434509},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6271048188209534},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6149927973747253},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.614234209060669},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5931041836738586},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5906076431274414},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5513854622840881},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49397116899490356},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.48242148756980896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3397335112094879},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1921293667","https://openalex.org/W1945616565","https://openalex.org/W1972675781","https://openalex.org/W2065151783","https://openalex.org/W2113290770","https://openalex.org/W2121056381","https://openalex.org/W2123024445","https://openalex.org/W2125389028","https://openalex.org/W2620038827","https://openalex.org/W2791953061","https://openalex.org/W2795282075","https://openalex.org/W2803187616","https://openalex.org/W2804077623","https://openalex.org/W2885593519","https://openalex.org/W2900175074","https://openalex.org/W2945007112","https://openalex.org/W2962682420","https://openalex.org/W2962762541","https://openalex.org/W2962974744","https://openalex.org/W2963081269","https://openalex.org/W2963160702","https://openalex.org/W2963207607","https://openalex.org/W2963371670","https://openalex.org/W2963389226","https://openalex.org/W2963697299","https://openalex.org/W2963735582","https://openalex.org/W2963744840","https://openalex.org/W2963759070","https://openalex.org/W2964153729","https://openalex.org/W2964155802","https://openalex.org/W2964234160","https://openalex.org/W2964253222","https://openalex.org/W2964273174","https://openalex.org/W2964292098","https://openalex.org/W2964309657","https://openalex.org/W2969215546","https://openalex.org/W3137695714","https://openalex.org/W4293846201","https://openalex.org/W6637162671","https://openalex.org/W6640298173","https://openalex.org/W6640425456","https://openalex.org/W6677082149","https://openalex.org/W6678280073","https://openalex.org/W6678470764","https://openalex.org/W6678815747","https://openalex.org/W6729756640","https://openalex.org/W6736098614","https://openalex.org/W6739868092","https://openalex.org/W6747898760","https://openalex.org/W6748965907","https://openalex.org/W6750523955","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6753772092","https://openalex.org/W6755816981","https://openalex.org/W6758632346","https://openalex.org/W6762892961","https://openalex.org/W6763485134"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W3194633786","https://openalex.org/W4401571341"],"abstract_inverted_index":{"Today's":[0],"available":[1],"datasets":[2],"in":[3,72],"the":[4,40,49,69,75,114,125,131,136,140,153,180,212],"wild,":[5],"e.g.,":[6],"from":[7,103,128,135],"social":[8],"media":[9],"and":[10,16,98,142,149,155,173,194],"open":[11],"platforms,":[12],"present":[13],"tremendous":[14],"opportunities":[15],"challenges":[17],"for":[18],"deep":[19,43,205],"learning,":[20],"as":[21,196,198],"there":[22],"is":[23,107],"a":[24,61,91,108,188],"significant":[25],"portion":[26],"of":[27,42,51,68,96,116,138,169,176,191,214],"tagged":[28],"images,":[29],"but":[30],"often":[31],"with":[32,195],"noisy,":[33],"i.e.":[34],"erroneous,":[35],"labels.":[36,53,145,216],"Recent":[37],"studies":[38],"improve":[39],"robustness":[41],"models":[44,206],"against":[45,187],"noisy":[46,70,80,141,157,167,215],"labels":[47,71,81,127,158],"without":[48],"knowledge":[50,137],"true":[52],"In":[54],"this":[55],"paper,":[56],"we":[57,89],"advocate":[58],"to":[59,74,159,203],"derive":[60],"stronger":[62],"classifier":[63,111],"which":[64,100,118],"proactively":[65,151],"makes":[66],"use":[67],"addition":[73],"original":[76],"images":[77,154],"-":[78],"turning":[79],"into":[82],"learning":[83],"features.":[84],"To":[85],"such":[86],"an":[87],"end,":[88],"propose":[90],"novel":[92],"framework,":[93],"LABELNET,":[94],"composed":[95],"Amateur":[97,106,129,148],"Expert,":[99,117],"iteratively":[101],"learn":[102],"each":[104],"other.":[105],"regular":[109],"image":[110,161],"trained":[112,147],"by":[113],"feedback":[115],"imitates":[119],"how":[120],"human":[121],"experts":[122],"would":[123],"correct":[124],"predicted":[126],"using":[130],"noise":[132,192],"pattern":[133],"learnt":[134],"both":[139],"ground":[143],"truth":[144],"The":[146],"Expert":[150],"leverage":[152],"their":[156],"infer":[160],"classes.":[162],"Our":[163],"empirical":[164],"evaluations":[165],"on":[166,210],"versions":[168],"MNIST,":[170],"CIFAR-10,":[171],"CIFAR-100":[172],"real-world":[174],"data":[175],"Clothing1M":[177],"show":[178],"that":[179,207],"proposed":[181],"model":[182],"can":[183],"achieve":[184],"robust":[185],"classification":[186],"wide":[189],"range":[190],"ratios":[193],"little":[197],"20-50%":[199],"training":[200],"data,":[201],"compared":[202],"state-of-the-art":[204],"solely":[208],"focus":[209],"distilling":[211],"impact":[213]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}