{"id":"https://openalex.org/W4394711732","doi":"https://doi.org/10.1109/tmm.2024.3387831","title":"Robust Image Classification With Noisy Labels by Negative Learning and Feature Space Renormalization","display_name":"Robust Image Classification With Noisy Labels by Negative Learning and Feature Space Renormalization","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4394711732","doi":"https://doi.org/10.1109/tmm.2024.3387831"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2024.3387831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2024.3387831","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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":null,"display_name":"Hao Wu","orcid":"https://orcid.org/0009-0006-7397-9709"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wu","raw_affiliation_strings":["School of IOT, Jiangnan University, Wuxi, China"],"raw_orcid":"https://orcid.org/0009-0006-7397-9709","affiliations":[{"raw_affiliation_string":"School of IOT, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100327203","display_name":"Jun Sun","orcid":"https://orcid.org/0000-0002-9824-4294"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Sun","raw_affiliation_strings":["School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"],"raw_orcid":"https://orcid.org/0000-0002-9824-4294","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111599522"],"apc_list":null,"apc_paid":null,"fwci":1.3892,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.8313462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"26","issue":null,"first_page":"9280","last_page":"9291"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9894999861717224,"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"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9879000186920166,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7654688954353333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6263689994812012},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6211915612220764},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5359021425247192},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.530025064945221},{"id":"https://openalex.org/keywords/renormalization","display_name":"Renormalization","score":0.5200355052947998},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5123676061630249},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48048344254493713},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4617483913898468},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3300721049308777},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.167995423078537}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7654688954353333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6263689994812012},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6211915612220764},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5359021425247192},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.530025064945221},{"id":"https://openalex.org/C166124518","wikidata":"https://www.wikidata.org/wiki/Q1047702","display_name":"Renormalization","level":2,"score":0.5200355052947998},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5123676061630249},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48048344254493713},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4617483913898468},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3300721049308777},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.167995423078537},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2024.3387831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2024.3387831","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4555050861","display_name":"\u6c42\u89e3\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\u4e2d\u590d\u6742\u4f18\u5316\u95ee\u9898\u7684\u6f14\u5316\u7b97\u6cd5\u7814\u7a76","funder_award_id":"61672263","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6112421292","display_name":null,"funder_award_id":"62272202","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1921293667","https://openalex.org/W2095705004","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2515770085","https://openalex.org/W2566079294","https://openalex.org/W2800791174","https://openalex.org/W2804847616","https://openalex.org/W2889978276","https://openalex.org/W2920830204","https://openalex.org/W2948606739","https://openalex.org/W2955147859","https://openalex.org/W2963697299","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2978625989","https://openalex.org/W2981873476","https://openalex.org/W2981952612","https://openalex.org/W3035336958","https://openalex.org/W3035682985","https://openalex.org/W3117488606","https://openalex.org/W3118608800","https://openalex.org/W3139144418","https://openalex.org/W3175103763","https://openalex.org/W3185386766","https://openalex.org/W3202897408","https://openalex.org/W3204785911","https://openalex.org/W3206067628","https://openalex.org/W4221158158","https://openalex.org/W4283705832","https://openalex.org/W4288083516","https://openalex.org/W4310745148","https://openalex.org/W4312465353","https://openalex.org/W4312601326","https://openalex.org/W4313135270","https://openalex.org/W4386075521","https://openalex.org/W4388514807","https://openalex.org/W6674330103","https://openalex.org/W6678280073","https://openalex.org/W6683307983","https://openalex.org/W6717772578","https://openalex.org/W6733814495","https://openalex.org/W6738471490","https://openalex.org/W6740005241","https://openalex.org/W6745136726","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6751795773","https://openalex.org/W6757248479","https://openalex.org/W6758632346","https://openalex.org/W6762161020","https://openalex.org/W6762563763","https://openalex.org/W6762913911","https://openalex.org/W6764051988","https://openalex.org/W6765939562","https://openalex.org/W6768848008","https://openalex.org/W6771630921","https://openalex.org/W6771787070","https://openalex.org/W6773005947","https://openalex.org/W6781063151","https://openalex.org/W6787972765","https://openalex.org/W6788329692","https://openalex.org/W6803462455","https://openalex.org/W6810073300","https://openalex.org/W6846971969","https://openalex.org/W6858208004"],"related_works":["https://openalex.org/W4379466091","https://openalex.org/W1034423453","https://openalex.org/W2158574015","https://openalex.org/W1625786414","https://openalex.org/W1549757625","https://openalex.org/W2061564169","https://openalex.org/W1993333995","https://openalex.org/W2050232943","https://openalex.org/W2565656575","https://openalex.org/W4390143830"],"abstract_inverted_index":{"Correctly":[0],"labeled":[1,97],"data":[2,32,98,154],"significantly":[3],"impacts":[4],"the":[5,19,29,38,93,96,104,110,120,125,133,141,152,166,190],"success":[6],"of":[7,21,31,95,119,132,168,194],"deep":[8,48],"learning":[9,49,88,106,127,143,158,172],"for":[10,171],"image":[11,71],"classification":[12,72],"and":[13,34,89,136,159,184,192],"other":[14],"computer":[15],"vision":[16],"tasks.":[17],"However,":[18],"accuracy":[20],"labels":[22,52,175],"annotated":[23],"by":[24,155],"humans":[25],"often":[26],"decreases":[27],"as":[28],"amount":[30],"increases,":[33],"a":[35,43,62,100,116,130,147],"rise":[36],"in":[37,70],"noisy":[39,51,74,174],"label":[40],"rate":[41],"deteriorates":[42],"neural":[44],"network's":[45],"performance.":[46],"Thus,":[47],"with":[50,73,173],"has":[53,99],"attracted":[54],"extensive":[55],"attention.":[56],"In":[57,76],"this":[58,77],"paper,":[59],"we":[60,108,123,145,164],"propose":[61,146],"novel":[63,148],"multi-network":[64],"method":[65,113,149,170],"that":[66],"achieves":[67],"robust":[68],"performances":[69],"labels.":[75],"method,":[78],"two":[79],"models":[80],"are":[81],"trained":[82],"at":[83],"each":[84],"epoch":[85],"using":[86,129],"supervised":[87],"semi-supervised":[90,105,126,142],"learning.":[91],"As":[92],"selection":[94,112],"great":[101],"impact":[102],"on":[103,176],"performance,":[107,144],"improve":[109],"sample":[111],"to":[114,150],"obtain":[115],"better":[117],"division":[118],"dataset.":[121],"Specifically,":[122],"divide":[124],"dataset":[128],"combination":[131],"per-sample":[134],"losses":[135],"model":[137],"memory.":[138],"To":[139],"enhance":[140],"train":[151],"unlabeled":[153],"combining":[156],"negative":[157],"feature":[160],"space":[161],"renormalization.":[162],"Finally,":[163],"verify":[165],"performance":[167],"our":[169,195],"four":[177],"benchmark":[178],"datasets,":[179],"which":[180],"include":[181],"Cifar10,":[182],"Cifar100":[183],"Clothing1M.":[185],"The":[186],"experimental":[187],"results":[188],"show":[189],"effectiveness":[191],"robustness":[193],"method.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
