{"id":"https://openalex.org/W4403488050","doi":"https://doi.org/10.3233/faia240648","title":"FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learning","display_name":"FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learning","publication_year":2024,"publication_date":"2024-10-16","ids":{"openalex":"https://openalex.org/W4403488050","doi":"https://doi.org/10.3233/faia240648"},"language":"en","primary_location":{"id":"doi:10.3233/faia240648","is_oa":false,"landing_page_url":"https://doi.org/10.3233/faia240648","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","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/A5104352434","display_name":"Huiling Qin","orcid":"https://orcid.org/0000-0002-4045-6091"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huiling Qin","raw_affiliation_strings":["Beijing Normal University"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052921346","display_name":"Xianyuan Zhan","orcid":"https://orcid.org/0000-0002-3683-0554"},"institutions":[{"id":"https://openalex.org/I4210108461","display_name":"Kingsoft (China)","ror":"https://ror.org/01stnfn33","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210108461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyuan Zhan","raw_affiliation_strings":["KingSoft"],"affiliations":[{"raw_affiliation_string":"KingSoft","institution_ids":["https://openalex.org/I4210108461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102020607","display_name":"Yuanxun Li","orcid":"https://orcid.org/0009-0005-7518-4005"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanxun Li","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101819994","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0002-8464-7797"},"institutions":[{"id":"https://openalex.org/I171589843","display_name":"JDA Software (United States)","ror":"https://ror.org/04s6h1250","country_code":"US","type":"company","lineage":["https://openalex.org/I171589843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["JD iCity, JD Technology, JD Intelligent Cities Research"],"affiliations":[{"raw_affiliation_string":"JD iCity, JD Technology, JD Intelligent Cities Research","institution_ids":["https://openalex.org/I171589843"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5104352434"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":1.5924,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.85277279,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.570900022983551,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.570900022983551,"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/T14470","display_name":"Advanced Data Processing Techniques","score":0.49380001425743103,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5814284086227417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3870285749435425}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5814284086227417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3870285749435425}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia240648","is_oa":false,"landing_page_url":"https://doi.org/10.3233/faia240648","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"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":{"Semi-supervised":[0],"learning":[1,24,49,98,130,178],"holds":[2],"great":[3],"promise":[4],"for":[5],"many":[6],"real-world":[7],"applications,":[8],"due":[9],"to":[10,13,34,92,117,140,153],"its":[11,151],"ability":[12],"leverage":[14],"both":[15,75],"unlabeled":[16,42,78],"and":[17,36,77,96,132],"expensive":[18],"labeled":[19,32,76,87],"data.":[20,43,88],"However,":[21],"most":[22],"semi-supervised":[23,48,129,177],"algorithms":[25],"still":[26],"heavily":[27],"rely":[28],"on":[29,86,103,156,161],"the":[30,38,52,82,101,174],"limited":[31],"data":[33],"infer":[35],"utilize":[37],"hidden":[39],"information":[40,73],"from":[41,74],"We":[44,145],"note":[45],"that":[46,136,169],"any":[47,104],"task":[50,59,131,135,143],"under":[51],"self-training":[53],"paradigm":[54],"also":[55],"hides":[56],"an":[57],"auxiliary":[58],"of":[60,72,84,114,150,165,176],"discriminating":[61],"label":[62,138],"observability.":[63],"Jointly":[64],"solving":[65],"these":[66],"two":[67],"tasks":[68],"allows":[69],"full":[70],"utilization":[71],"data,":[79],"thus":[80],"alleviating":[81],"problem":[83],"over-reliance":[85],"This":[89],"naturally":[90],"leads":[91],"a":[93,119,126,133,162],"new":[94],"generic":[95],"efficient":[97],"framework":[99],"without":[100],"reliance":[102],"domain-specific":[105],"information,":[106],"which":[107,122],"we":[108,167],"call":[109],"FlexSSL.":[110],"The":[111],"key":[112],"idea":[113],"FlexSSL":[115,170],"is":[116],"construct":[118],"semi-cooperative":[120],"\u201cgame\u201d,":[121],"forges":[123],"cooperation":[124],"between":[125],"main":[127,142],"self-interested":[128],"companion":[134],"infers":[137],"observability":[139],"facilitate":[141],"training.":[144],"show":[146],"with":[147],"theoretical":[148],"derivation":[149],"connection":[152],"loss":[154],"re-weighting":[155],"noisy":[157],"labels.":[158],"Through":[159],"evaluations":[160],"diverse":[163],"range":[164],"tasks,":[166],"demonstrate":[168],"can":[171],"consistently":[172],"enhance":[173],"performance":[175],"algorithms.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}