{"id":"https://openalex.org/W3014246171","doi":"https://doi.org/10.5220/0008940900230032","title":"Self-Training using Selection Network for Semi-supervised Learning","display_name":"Self-Training using Selection Network for Semi-supervised Learning","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3014246171","doi":"https://doi.org/10.5220/0008940900230032","mag":"3014246171"},"language":"en","primary_location":{"id":"doi:10.5220/0008940900230032","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0008940900230032","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods","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/A5073922223","display_name":"Jisoo Jeong","orcid":"https://orcid.org/0009-0004-7809-4884"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jisoo Jeong","raw_affiliation_strings":["Seoul National University, Seoul, South Korea, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea, --- Select a Country ---","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022273533","display_name":"Seungeui Lee","orcid":"https://orcid.org/0000-0003-3560-785X"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungeui Lee","raw_affiliation_strings":["Seoul National University, Seoul, South Korea, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea, --- Select a Country ---","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084897975","display_name":"Nojun Kwak","orcid":"https://orcid.org/0000-0002-1792-0327"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nojun Kwak","raw_affiliation_strings":["Seoul National University, Seoul, South Korea, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea, --- Select a Country ---","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073922223"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.4114,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68399323,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"32"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9975000023841858,"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/T10320","display_name":"Neural Networks and Applications","score":0.9975000023841858,"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":0.996999979019165,"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":0.9837999939918518,"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":0.7664504051208496},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6346640586853027},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6327270269393921},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6139894723892212},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5375561118125916}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664504051208496},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6346640586853027},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6327270269393921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6139894723892212},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5375561118125916},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0008940900230032","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0008940900230032","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},{"id":"pmh:oai:s-space.snu.ac.kr:10371/206041","is_oa":false,"landing_page_url":"https://hdl.handle.net/10371/206041","pdf_url":null,"source":{"id":"https://openalex.org/S4306401345","display_name":"Seoul National University Open Repository (Seoul National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139264467","host_organization_name":"Seoul National University","host_organization_lineage":["https://openalex.org/I139264467"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W830076066","https://openalex.org/W1663973292","https://openalex.org/W1975165783","https://openalex.org/W2048679005","https://openalex.org/W2079057609","https://openalex.org/W2095705004","https://openalex.org/W2099471712","https://openalex.org/W2117539524","https://openalex.org/W2136504847","https://openalex.org/W2164341120","https://openalex.org/W2164598857","https://openalex.org/W2183646625","https://openalex.org/W2335728318","https://openalex.org/W2592691248","https://openalex.org/W2740917334","https://openalex.org/W2949416428","https://openalex.org/W2963296333","https://openalex.org/W2963685250","https://openalex.org/W3118608800"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1],"(SSL)":[2],"is":[3,67,99],"a":[4,9,105],"study":[5],"that":[6,31],"efficiently":[7],"exploits":[8],"large":[10],"amount":[11],"of":[12,20,25,34,42,44,111,119,189,196],"unlabeled":[13,35,56,62,92,112,137,190],"data":[14,36,63,113,131,191],"to":[15,89,101,104,151,172],"improve":[16],"performance":[17,183],"in":[18,39,94],"conditions":[19],"limited":[21],"labeled":[22,45,121,135,198],"data.":[23,46,122,199],"Most":[24],"the":[26,32,40,61,95,117,120,124,134,140,144,166,173,187,197],"conventional":[27,125,153,167],"SSL":[28,79,126,154,163,175],"methods":[29,50,168],"assume":[30],"classes":[33,43,110,188],"are":[37,114,192],"included":[38],"set":[41],"In":[47,73],"addition,":[48],"these":[49],"do":[51],"not":[52,68,147,180],"sort":[53],"out":[54],"useless":[55],"samples":[57,138],"and":[58,136],"use":[59],"all":[60],"for":[64,70],"learning,":[65],"which":[66,85,128],"suitable":[69],"realistic":[71],"situations.":[72],"this":[74],"paper,":[75],"we":[76],"propose":[77],"an":[78],"method":[80,146,178],"called":[81],"selective":[82],"self-training":[83],"(SST),":[84],"selectively":[86],"decides":[87],"whether":[88],"include":[90],"each":[91],"sample":[93],"training":[96],"process.":[97],"It":[98],"designed":[100],"be":[102,159,170],"applied":[103,171],"more":[106],"real":[107],"situation":[108],"where":[109,132],"different":[115,193],"from":[116,194],"ones":[118],"For":[123],"problems":[127],"deal":[129],"with":[130,161],"both":[133],"share":[139],"same":[141],"class":[142],"categories,":[143],"proposed":[145],"only":[148],"performs":[149],"comparable":[150],"other":[152,162],"algorithms":[155],"but":[156],"also":[157],"can":[158],"combined":[160],"algorithms.":[164],"While":[165],"cannot":[169],"new":[174],"problems,":[176],"our":[177],"does":[179],"show":[181],"any":[182],"degradation":[184],"even":[185],"if":[186],"those":[195]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}