{"id":"https://openalex.org/W4410949666","doi":"https://doi.org/10.1109/lsp.2025.3575594","title":"Learning Label Perturbations","display_name":"Learning Label Perturbations","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410949666","doi":"https://doi.org/10.1109/lsp.2025.3575594"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2025.3575594","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3575594","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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":"https://openalex.org/A5101114453","display_name":"Xiao Gong","orcid":"https://orcid.org/0000-0003-0345-5986"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao Gong","raw_affiliation_strings":["Department of Mathematics, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-0345-5986","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058201402","display_name":"Zifan Song","orcid":"https://orcid.org/0000-0001-8734-9878"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zifan Song","raw_affiliation_strings":["Department of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-8734-9878","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075333422","display_name":"Guosheng Hu","orcid":"https://orcid.org/0000-0002-9448-9892"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guosheng Hu","raw_affiliation_strings":["Oosto, Belfast, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oosto, Belfast, U.K","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064237960","display_name":"Cairong Zhao","orcid":"https://orcid.org/0000-0001-6745-9674"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cairong Zhao","raw_affiliation_strings":["Department of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6745-9674","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101114453"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05518963,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"2359","last_page":"2363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9973000288009644,"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":0.9973000288009644,"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":0.9768000245094299,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9731000065803528,"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":0.5575580596923828},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3834487199783325}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5575580596923828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3834487199783325}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3575594","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3575594","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1821462560","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2160715776","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2345474290","https://openalex.org/W2481910659","https://openalex.org/W2597603852","https://openalex.org/W2788686132","https://openalex.org/W2798405286","https://openalex.org/W2904170036","https://openalex.org/W2963163009","https://openalex.org/W2981873476","https://openalex.org/W3034756453","https://openalex.org/W3118608800","https://openalex.org/W4378966527","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6678280073","https://openalex.org/W6748034479","https://openalex.org/W6787972765","https://openalex.org/W6839568498"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Supervised":[0],"learning":[1,108],"typically":[2],"uses":[3,65],"hard":[4,76],"labels":[5,77,80,134],"for":[6,81,135],"annotations,":[7],"which":[8,64],"may":[9],"not":[10,51],"fully":[11],"capture":[12],"the":[13,17,20,33,45,53,57,66,99,120,126],"underlying":[14],"distribution":[15,69],"of":[16,41,70],"data.":[18,58],"In":[19,92],"literature,":[21],"label":[22,106],"smoothing":[23],"is":[24,61],"a":[25,38,71,82,89,104],"method":[26,87,109,117],"that":[27,143],"can":[28],"reduce":[29],"overconfidence":[30],"and":[31,44,102,128,137,155],"enhance":[32],"model":[34,127,147],"generalization":[35],"by":[36],"using":[37],"weighted":[39],"average":[40],"one-hot":[42],"vectors":[43],"uniform":[46],"distribution,":[47],"but":[48],"it":[49],"does":[50],"extract":[52],"intrinsic":[54],"information":[55,122],"from":[56,98],"Another":[59],"approach":[60],"knowledge":[62],"distillation,":[63],"predicted":[67],"probability":[68],"teacher":[72],"network":[73],"trained":[74],"with":[75],"as":[78],"soft":[79],"student":[83],"network.":[84],"However,":[85],"this":[86,93],"lacks":[88],"theoretical":[90],"explanation.":[91],"work,":[94],"we":[95],"draw":[96],"inspiration":[97],"influence":[100],"function":[101],"propose":[103],"post-hoc":[105],"perturbation":[107],"called":[110],"Deep":[111],"Soft":[112],"Label":[113],"Learning":[114],"(DSLL).":[115],"This":[116],"iteratively":[118],"leverages":[119],"inherent":[121],"present":[123],"in":[124],"both":[125],"data":[129],"to":[130],"theoretically":[131],"determine":[132],"optimal":[133],"classification":[136,154],"regression":[138],"problems.":[139],"Our":[140],"experiments":[141],"demonstrate":[142],"DSLL":[144],"consistently":[145],"enhances":[146],"performance":[148],"across":[149],"various":[150],"tasks,":[151],"including":[152],"image":[153],"object":[156],"detection.":[157]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
