{"id":"https://openalex.org/W2997393112","doi":"https://doi.org/10.1109/access.2019.2962258","title":"Semi-Supervised Learning for Fine-Grained Classification With Self-Training","display_name":"Semi-Supervised Learning for Fine-Grained Classification With Self-Training","publication_year":2019,"publication_date":"2019-12-25","ids":{"openalex":"https://openalex.org/W2997393112","doi":"https://doi.org/10.1109/access.2019.2962258","mag":"2997393112"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2962258","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2962258","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08943213.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08943213.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016430669","display_name":"Obed Tettey Nartey","orcid":"https://orcid.org/0000-0003-3072-6833"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Obed Tettey Nartey","raw_affiliation_strings":["Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-3072-6833","affiliations":[{"raw_affiliation_string":"Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102782898","display_name":"Guowu Yang","orcid":"https://orcid.org/0000-0002-1725-4598"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I167274908","display_name":"Guangxi University for Nationalities","ror":"https://ror.org/0495efn48","country_code":"CN","type":"education","lineage":["https://openalex.org/I167274908"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guowu Yang","raw_affiliation_strings":["Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, China"],"raw_orcid":"https://orcid.org/0000-0002-1725-4598","affiliations":[{"raw_affiliation_string":"Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, China","institution_ids":["https://openalex.org/I167274908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102894562","display_name":"Jinzhao Wu","orcid":"https://orcid.org/0000-0002-0272-7487"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]},{"id":"https://openalex.org/I167274908","display_name":"Guangxi University for Nationalities","ror":"https://ror.org/0495efn48","country_code":"CN","type":"education","lineage":["https://openalex.org/I167274908"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinzhao Wu","raw_affiliation_strings":["Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, China","School of Computer Science and Electronic Information, Guangxi University, Nanning, China"],"raw_orcid":"https://orcid.org/0000-0002-0272-7487","affiliations":[{"raw_affiliation_string":"Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, China","institution_ids":["https://openalex.org/I167274908"]},{"raw_affiliation_string":"School of Computer Science and Electronic Information, Guangxi University, Nanning, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041192515","display_name":"Sarpong Kwadwo Asare","orcid":"https://orcid.org/0000-0002-0992-4512"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sarpong Kwadwo Asare","raw_affiliation_strings":["School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-0992-4512","affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016430669"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.6126,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.92310994,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"2109","last_page":"2121"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.992900013923645,"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/overfitting","display_name":"Overfitting","score":0.9358970522880554},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8084056973457336},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.730597972869873},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6351538896560669},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.6035261750221252},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5906574726104736},{"id":"https://openalex.org/keywords/mistake","display_name":"Mistake","score":0.5589315295219421},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5560250282287598},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49814867973327637},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4938414692878723},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.47342541813850403},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4554942548274994},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.445926308631897},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.44218528270721436},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2872684597969055}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9358970522880554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8084056973457336},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.730597972869873},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6351538896560669},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.6035261750221252},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5906574726104736},{"id":"https://openalex.org/C2777179996","wikidata":"https://www.wikidata.org/wiki/Q911222","display_name":"Mistake","level":2,"score":0.5589315295219421},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5560250282287598},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49814867973327637},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4938414692878723},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.47342541813850403},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4554942548274994},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.445926308631897},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.44218528270721436},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2872684597969055},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2962258","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2962258","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08943213.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:da8636969d0f4e54bdc23d1ce3470fed","is_oa":true,"landing_page_url":"https://doaj.org/article/da8636969d0f4e54bdc23d1ce3470fed","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 2109-2121 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2962258","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2962258","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08943213.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2294421742","display_name":null,"funder_award_id":"2016AD05050","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3015965114","display_name":null,"funder_award_id":"AB17129012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3580067134","display_name":null,"funder_award_id":"AA17204096","funder_id":"https://openalex.org/F4320335965","funder_display_name":"Science and Technology Major Project of Guangxi"},{"id":"https://openalex.org/G3759245107","display_name":null,"funder_award_id":"61572109","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4637933310","display_name":null,"funder_award_id":"AA17204096","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8034016658","display_name":null,"funder_award_id":"61772006","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"},{"id":"https://openalex.org/F4320335965","display_name":"Science and Technology Major Project of Guangxi","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2997393112.pdf","grobid_xml":"https://content.openalex.org/works/W2997393112.grobid-xml"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W143094320","https://openalex.org/W259338706","https://openalex.org/W1493009343","https://openalex.org/W1522301498","https://openalex.org/W1596077776","https://openalex.org/W1616462885","https://openalex.org/W1686810756","https://openalex.org/W1797268635","https://openalex.org/W1904365287","https://openalex.org/W1995543189","https://openalex.org/W2044074081","https://openalex.org/W2079057609","https://openalex.org/W2091759811","https://openalex.org/W2097117768","https://openalex.org/W2101210369","https://openalex.org/W2106401878","https://openalex.org/W2108598243","https://openalex.org/W2110015572","https://openalex.org/W2112796928","https://openalex.org/W2115553938","https://openalex.org/W2117729721","https://openalex.org/W2129492014","https://openalex.org/W2133434696","https://openalex.org/W2136504847","https://openalex.org/W2138011018","https://openalex.org/W2155904486","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2229419338","https://openalex.org/W2287418003","https://openalex.org/W2306952455","https://openalex.org/W2342242867","https://openalex.org/W2533598788","https://openalex.org/W2588991089","https://openalex.org/W2619296796","https://openalex.org/W2765861484","https://openalex.org/W2788159735","https://openalex.org/W2807931652","https://openalex.org/W2883888092","https://openalex.org/W2895281799","https://openalex.org/W2963446712","https://openalex.org/W2963500702","https://openalex.org/W2963542991","https://openalex.org/W2964350391","https://openalex.org/W2982204884","https://openalex.org/W3118608800","https://openalex.org/W3124039814","https://openalex.org/W4361805498","https://openalex.org/W6605856410","https://openalex.org/W6629368666","https://openalex.org/W6631190155","https://openalex.org/W6636475194","https://openalex.org/W6637373629","https://openalex.org/W6638319203","https://openalex.org/W6640036494","https://openalex.org/W6677439797","https://openalex.org/W6679805309","https://openalex.org/W6680140577","https://openalex.org/W6684191040","https://openalex.org/W6694260854","https://openalex.org/W6698041030","https://openalex.org/W6753209683","https://openalex.org/W6765961784","https://openalex.org/W6787972765","https://openalex.org/W6910223128","https://openalex.org/W6910631559"],"related_works":["https://openalex.org/W4312414840","https://openalex.org/W2794908468","https://openalex.org/W4206276646","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W3004135598","https://openalex.org/W2952937263","https://openalex.org/W2131153761"],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1,5,214],"is":[2,59],"a":[3,13,28,50,60],"machine":[4],"approach":[6],"that":[7,54,188],"tackles":[8],"the":[9,34,43,82,89,96,110,115,118,125,130,133,140,145,152,155,178,200,203,212],"challenge":[10],"of":[11,16,36,45,135,147,154,211],"having":[12],"large":[14],"set":[15,84,142],"unlabeled":[17,70,83,103],"data":[18,71],"and":[19,41,72,108,113,138,173],"few":[20],"labeled":[21,90],"ones.":[22],"In":[23],"this":[24],"paper":[25],"we":[26,77,94],"adopt":[27],"semi-supervised":[29],"self-training":[30,65],"method":[31,123],"to":[32,85,88,143],"increase":[33],"amount":[35],"training":[37,91,111,120,141],"data,":[38,112],"prevent":[39],"overfitting":[40],"improve":[42],"performance":[44,194],"deep":[46,148],"models":[47],"by":[48,157],"proposing":[49],"novel":[51],"selection":[52],"algorithm":[53],"prevents":[55],"mistake":[56],"reinforcement":[57],"which":[58,165],"common":[61],"thing":[62],"in":[63,127,132],"conventional":[64],"models.":[66,149,215],"The":[67,122],"model":[68,156,179,204],"leverages,":[69],"specifically,":[73],"after":[74],"each":[75,102],"training,":[76],"first":[78],"generate":[79],"pseudo-labels":[80,107],"on":[81,117,160,180,199],"be":[86],"added":[87],"samples.":[92],"Next,":[93],"select":[95],"top-k":[97],"most-confident":[98],"pseudo-labeled":[99],"images":[100],"from":[101],"class":[104],"with":[105],"their":[106],"update":[109],"retrain":[114],"network":[116],"updated":[119],"data.":[121,183],"improves":[124],"accuracy":[126,208],"two-fold;":[128],"bridging":[129],"gap":[131],"appearance":[134],"visual":[136],"objects,":[137],"enlarging":[139],"meet":[144],"demands":[146],"We":[150,175],"demonstrated":[151],"effectiveness":[153],"conducting":[158],"experiments":[159],"four":[161],"state-of-the-art":[162],"fine-grained":[163],"datasets,":[164],"include":[166],"Stanford":[167,169],"Dogs,":[168],"Cars,":[170],"102-Oxford":[171],"flowers,":[172],"CUB-200-2011.":[174],"further":[176],"evaluated":[177],"some":[181,196],"coarse-grain":[182],"Experimental":[184],"results":[185],"clearly":[186],"show":[187],"our":[189],"proposed":[190],"framework":[191],"has":[192],"better":[193],"than":[195,209],"previous":[197],"works":[198],"same":[201],"data;":[202],"obtained":[205],"higher":[206],"classification":[207],"most":[210],"supervised":[213]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6}],"updated_date":"2026-05-15T08:27:34.491423","created_date":"2025-10-10T00:00:00"}
