乐胖代购免代理版

{"id":"https://openalex.org/W4200448705","doi":"https://doi.org/10.1109/iccais52680.2021.9624662","title":"Learning from Noisy Labeled Data via Sharpen Prediction Loss and Re-Correction","display_name":"Learning from Noisy Labeled Data via Sharpen Prediction Loss and Re-Correction","publication_year":2021,"publication_date":"2021-10-14","ids":{"openalex":"https://openalex.org/W4200448705","doi":"https://doi.org/10.1109/iccais52680.2021.9624662"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais52680.2021.9624662","pdf_url":null,"source":{"id":"https://openalex.org/S4363608071","display_name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5100693499","display_name":"Juncheng Wang","orcid":"https://orcid.org/0009-0008-2387-8904"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juncheng Wang","raw_affiliation_strings":["School of Automation, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035112957","display_name":"Siyue Ren","orcid":"https://orcid.org/0000-0003-4027-1692"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyue Ren","raw_affiliation_strings":["School of Automation, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033213528","display_name":"Jie Geng","orcid":"https://orcid.org/0000-0003-4858-823X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Geng","raw_affiliation_strings":["School of Information and Electronics, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":57},"biblio":{"volume":null,"issue":null,"first_page":"537","last_page":"542"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9999,"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":0.9999,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.992,"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":0.9874,"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/overfitting","display_name":"Overfitting","score":0.88603294},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.6026258},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep Neural Networks","score":0.4719887},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.43073538}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.88603294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7982048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.682096},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6677899},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.61232936},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.6026258},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5112934},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5057986},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48598227},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4719887},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.44991988},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.43073538},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.41524804},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40622967},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36952725},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.1963728},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais52680.2021.9624662","pdf_url":null,"source":{"id":"https://openalex.org/S4363608071","display_name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.41}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1866072925","https://openalex.org/W1921293667","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2112841646","https://openalex.org/W2121056381","https://openalex.org/W2302255633","https://openalex.org/W2620673134","https://openalex.org/W2951863938","https://openalex.org/W2963096987","https://openalex.org/W2963509340","https://openalex.org/W2963735582","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2967052791","https://openalex.org/W2996108195","https://openalex.org/W3105616927","https://openalex.org/W3137695714","https://openalex.org/W4239072543","https://openalex.org/W4244259635"],"related_works":["https://openalex.org/W4309960894","https://openalex.org/W3172936821","https://openalex.org/W3113301541","https://openalex.org/W3042497982","https://openalex.org/W2978625989","https://openalex.org/W2963735582","https://openalex.org/W2951459130","https://openalex.org/W2803237185","https://openalex.org/W2786764570","https://openalex.org/W1575623062"],"abstract_inverted_index":{"Deep":[0,37],"learning":[1,72],"has":[2],"achieved":[3],"excellent":[4],"results":[5,140],"in":[6],"many":[7],"applications":[8],"with":[9,40,63,144],"a":[10,47,60],"large":[11],"number":[12],"of":[13,84],"high-quality":[14,24],"annotation":[15],"datasets.":[16],"However,":[17],"it":[18,28],"is":[19,69,104,113,123],"difficult":[20],"to":[21,45,56,79,115,125],"obtain":[22],"numerous":[23],"labeled":[25,32,42,75,146],"samples,":[26,89],"since":[27],"may":[29],"generate":[30],"noisy":[31,41,74,145],"data":[33,43,147],"during":[34],"manually":[35],"annotating.":[36],"neural":[38],"network":[39],"leads":[44],"overfitting":[46],"nd":[48],"greatly":[49],"affects":[50],"t":[51],"he":[52],"performance.":[53],"In":[54,97],"order":[55],"overcome":[57],"the":[58,81,98,101,118,135],"issue,":[59],"deep":[61,102],"model":[62,103,152],"sharpen":[64,110],"prediction":[65,111],"loss":[66,82,112],"and":[67,87,90,121,128],"re-correction":[68],"proposed":[70,99,114,151],"for":[71],"from":[73],"data,":[76],"which":[77],"aims":[78],"modify":[80],"distributions":[83],"noise":[85,92,127],"samples":[86,93,130],"clean":[88,129],"eliminate":[91],"by":[94],"unsupervised":[95],"clustering.":[96],"framework,":[100],"warmed":[105],"up":[106],"through":[107],"several":[108],"epochs,":[109],"effectively":[116],"measure":[117],"sample":[119],"loss,":[120],"recorrection":[122],"utilized":[124],"separate":[126],"as":[131,133],"well":[132],"predict":[134],"final":[136],"l":[137],"abels.":[138],"Experimental":[139],"on":[141],"two":[142],"datasets":[143],"demonstrate":[148],"that":[149],"our":[150],"yields":[153],"superior":[154],"classification":[155],"accuracies.":[156]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4200448705","counts_by_year":[],"updated_date":"2025-01-26T21:26:52.426243","created_date":"2021-12-31"}