{"id":"https://openalex.org/W2892005688","doi":"https://doi.org/10.1109/icip.2018.8451331","title":"Cyclic Annealing Training Convolutional Neural Networks for Image Classification with Noisy Labels","display_name":"Cyclic Annealing Training Convolutional Neural Networks for Image Classification with Noisy Labels","publication_year":2018,"publication_date":"2018-09-07","ids":{"openalex":"https://openalex.org/W2892005688","doi":"https://doi.org/10.1109/icip.2018.8451331","mag":"2892005688"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2018.8451331","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451331","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","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/A5017567548","display_name":"Jiawei Li","orcid":"https://orcid.org/0000-0002-3102-932X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiawei Li","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023762528","display_name":"Tao Dai","orcid":"https://orcid.org/0000-0003-0594-6404"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Dai","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059534557","display_name":"Qingtao Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingtao Tang","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047134950","display_name":"Yeli Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yeli Xing","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034104790","display_name":"Shu\u2010Tao Xia","orcid":"https://orcid.org/0000-0002-8639-982X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu-Tao Xia","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017567548"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.6515,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76941085,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"21","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9995999932289124,"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":0.9995999932289124,"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":0.984499990940094,"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/T10057","display_name":"Face and Expression Recognition","score":0.9728000164031982,"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":0.7692064046859741},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7069002985954285},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.666127622127533},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5775039792060852},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5774216055870056},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.49709632992744446},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43030422925949097},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41043180227279663},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.31530410051345825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7692064046859741},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7069002985954285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.666127622127533},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5775039792060852},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5774216055870056},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.49709632992744446},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43030422925949097},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41043180227279663},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31530410051345825},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2018.8451331","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451331","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","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":35,"referenced_works":["https://openalex.org/W62203920","https://openalex.org/W605727707","https://openalex.org/W1605688901","https://openalex.org/W1768290534","https://openalex.org/W1866072925","https://openalex.org/W1994550352","https://openalex.org/W2012243161","https://openalex.org/W2112076978","https://openalex.org/W2113290770","https://openalex.org/W2121056381","https://openalex.org/W2138507544","https://openalex.org/W2167460663","https://openalex.org/W2194775991","https://openalex.org/W2252268321","https://openalex.org/W2403681572","https://openalex.org/W2492899067","https://openalex.org/W2511730936","https://openalex.org/W2571047004","https://openalex.org/W2612983688","https://openalex.org/W2912934387","https://openalex.org/W2951696358","https://openalex.org/W2962762541","https://openalex.org/W2963263347","https://openalex.org/W2963446085","https://openalex.org/W2963446712","https://openalex.org/W2973562770","https://openalex.org/W3104436273","https://openalex.org/W3118608800","https://openalex.org/W4212883601","https://openalex.org/W4232478844","https://openalex.org/W6602535662","https://openalex.org/W6639331287","https://openalex.org/W6691441656","https://openalex.org/W6713348437","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666","https://openalex.org/W2551249631","https://openalex.org/W3014952856","https://openalex.org/W2964843961"],"abstract_inverted_index":{"Noisy":[0],"labels":[1,19,129,142],"modeling":[2,20,143],"makes":[3],"a":[4,42],"convolutional":[5],"neural":[6],"network":[7],"(CNN)":[8],"more":[9],"robust":[10],"for":[11],"the":[12,31,50,57,63,83,93,101,107,112,118,132],"image":[13,123],"classification":[14,124],"problem.":[15],"However,":[16],"current":[17],"noisy":[18,128,141],"methods":[21],"usually":[22],"require":[23],"an":[24],"expectation-maximization":[25],"(EM)":[26],"based":[27],"procedure":[28],"to":[29,47,91,110],"optimize":[30],"parameters,":[32],"which":[33],"is":[34,59],"computationally":[35],"expensive.":[36],"In":[37,89],"this":[38],"paper,":[39],"we":[40,79],"utilize":[41],"fast":[43],"annealing":[44],"training":[45,52,58,77,94],"method":[46,120],"speed":[48],"up":[49],"CNN":[51,73,104],"in":[53],"every":[54,76],"M-step.":[55],"Since":[56],"repeated":[60],"executed":[61],"along":[62],"entire":[64],"EM":[65],"optimization":[66],"path":[67],"and":[68,131],"obtain":[69],"many":[70],"local":[71,102],"minimal":[72,103],"models":[74,105],"from":[75],"cycle,":[78],"name":[80],"it":[81],"as":[82],"Cyclic":[84],"Annealing":[85],"Training":[86],"(CAT)":[87],"approach.":[88],"addition":[90],"reducing":[92],"time,":[95],"CAT":[96,137],"can":[97],"further":[98],"bagging":[99],"all":[100],"at":[106],"test":[108],"time":[109],"improve":[111],"performance":[113],"of":[114],"classification.":[115],"We":[116],"evaluate":[117],"proposed":[119],"on":[121],"several":[122],"datasets":[125],"with":[126],"different":[127],"patterns,":[130],"results":[133],"show":[134],"that":[135],"our":[136],"approach":[138],"outperforms":[139],"state-of-the-art":[140],"methods.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
