{"id":"https://openalex.org/W3002172179","doi":"https://doi.org/10.1109/vcip47243.2019.8965861","title":"Competing Ratio Loss for Multi-class image Classification","display_name":"Competing Ratio Loss for Multi-class image Classification","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3002172179","doi":"https://doi.org/10.1109/vcip47243.2019.8965861","mag":"3002172179"},"language":"en","primary_location":{"id":"doi:10.1109/vcip47243.2019.8965861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Visual Communications and Image Processing (VCIP)","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/A5100365767","display_name":"Ke Zhang","orcid":"https://orcid.org/0000-0003-3271-3585"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ke Zhang","raw_affiliation_strings":["North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","institution_ids":["https://openalex.org/I153473198"]},{"raw_affiliation_string":"Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115547209","display_name":"Xinsheng Wang","orcid":"https://orcid.org/0000-0002-7528-4621"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinsheng Wang","raw_affiliation_strings":["North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","institution_ids":["https://openalex.org/I153473198"]},{"raw_affiliation_string":"Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103161424","display_name":"Yurong Guo","orcid":"https://orcid.org/0000-0003-3241-0701"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yurong Guo","raw_affiliation_strings":["North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","institution_ids":["https://openalex.org/I153473198"]},{"raw_affiliation_string":"Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085263743","display_name":"Zhenbing Zhao","orcid":"https://orcid.org/0000-0003-2290-0598"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenbing Zhao","raw_affiliation_strings":["North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University,Department of Electronic and Communication Engineering,Hebei,China","institution_ids":["https://openalex.org/I153473198"]},{"raw_affiliation_string":"Department of Electronic and Communication Engineering, North China Electric Power University, Hebei, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039812471","display_name":"Zhanyu Ma","orcid":"https://orcid.org/0000-0003-2950-2488"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanyu Ma","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Pattern Recognition and Intelligent System Laboratory,Beijing,China","Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Pattern Recognition and Intelligent System Laboratory,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100365767"],"corresponding_institution_ids":["https://openalex.org/I153473198"],"apc_list":null,"apc_paid":null,"fwci":0.3037,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.62247424,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9994000196456909,"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/cross-entropy","display_name":"Cross entropy","score":0.7026414275169373},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6908769011497498},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.64580237865448},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6186049580574036},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5815576910972595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5811403393745422},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.547271728515625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5469293594360352},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5118440985679626},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.45103439688682556},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4382554590702057},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.43628332018852234},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43105366826057434},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.424545019865036},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3902427554130554},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32515907287597656},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2567308247089386}],"concepts":[{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.7026414275169373},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6908769011497498},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.64580237865448},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6186049580574036},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5815576910972595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5811403393745422},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.547271728515625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5469293594360352},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5118440985679626},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.45103439688682556},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4382554590702057},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.43628332018852234},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43105366826057434},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.424545019865036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3902427554130554},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32515907287597656},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2567308247089386},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip47243.2019.8965861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2098368939","https://openalex.org/W2132083787","https://openalex.org/W2156909104","https://openalex.org/W2194775991","https://openalex.org/W2520774990","https://openalex.org/W2557283755","https://openalex.org/W2885311373","https://openalex.org/W2895831313","https://openalex.org/W2919115771","https://openalex.org/W2947109354","https://openalex.org/W2962949934","https://openalex.org/W2963351448","https://openalex.org/W2963446712","https://openalex.org/W2963656735","https://openalex.org/W2980025636","https://openalex.org/W3118608800","https://openalex.org/W6637373629","https://openalex.org/W6726946684","https://openalex.org/W6735013348","https://openalex.org/W6750448517","https://openalex.org/W6753278433","https://openalex.org/W6755085369","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W9190101","https://openalex.org/W9362070","https://openalex.org/W4275953","https://openalex.org/W8198582","https://openalex.org/W11339170","https://openalex.org/W12336802","https://openalex.org/W3030276","https://openalex.org/W1368183","https://openalex.org/W4919037","https://openalex.org/W3338805"],"abstract_inverted_index":{"Cross-entropy":[0],"loss":[1,82,116],"function":[2,83],"(CEL)":[3],"is":[4,66,84,98,157],"widely":[5],"used":[6],"for":[7],"training":[8,41,92],"a":[9,88],"multi-class":[10],"classification":[11,25],"deep":[12,172],"convolutional":[13,173],"neural":[14,174],"network":[15],"(DCNN).":[16],"While":[17],"CEL":[18,179],"has":[19],"been":[20],"successfully":[21],"implemented":[22],"in":[23],"image":[24],"tasks,":[26],"it":[27],"only":[28],"focuses":[29],"on":[30,171,182],"the":[31,38,50,71,81,94,106,120,125,135,138,141,145,153,165],"posterior":[32,121],"probability":[33,122,139],"of":[34,40,80,96,108,140,147,155,169],"correct":[35,55,72,126,142],"class":[36,56,73,127,143],"when":[37],"labels":[39],"images":[42],"are":[43],"one-hot.":[44],"It":[45],"cannot":[46],"be":[47],"discriminated":[48],"against":[49],"classes":[51,131],"not":[52,99],"belong":[53],"to":[54,68,132],"(wrong":[57],"classes)":[58],"directly.":[59],"Negative":[60],"Log":[61],"Likelihood":[62],"Ratio":[63],"Loss":[64],"(NLLR)":[65],"proposed":[67],"better":[69,133],"discriminate":[70],"from":[74],"competing":[75,114,129],"wrong":[76,130,148],"classes.":[77],"But":[78],"optimization":[79],"normally":[85],"presented":[86],"as":[87],"minimization":[89],"problem.":[90],"In":[91],"DCNN,":[93],"value":[95,154],"NLLR":[97,109,181],"constantly":[100,158],"positive":[101],"or":[102],"negative,":[103],"which":[104,118,150],"affects":[105],"convergence":[107],"adversely.":[110],"So,":[111],"we":[112,163],"propose":[113],"ratio":[115,123],"(CRL),":[117],"calculates":[119],"between":[124,137],"and":[128,144,167,180],"widen":[134],"difference":[136],"probabilities":[146],"classes,":[149],"also":[151],"assures":[152],"CRL":[156,170,177],"positive.":[159],"Through":[160],"massive":[161],"experiments,":[162],"demonstrate":[164],"effectiveness":[166],"robustness":[168],"networks,":[175],"our":[176],"outperforms":[178],"CIFAR-10/100":[183],"datasets.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
