{"id":"https://openalex.org/W4388739267","doi":"https://doi.org/10.1109/tpami.2023.3333528","title":"Negatives Make a Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning","display_name":"Negatives Make a Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning","publication_year":2023,"publication_date":"2023-11-16","ids":{"openalex":"https://openalex.org/W4388739267","doi":"https://doi.org/10.1109/tpami.2023.3333528","pmid":"https://pubmed.ncbi.nlm.nih.gov/37971914"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2023.3333528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3333528","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5066964304","display_name":"Xiu-Shen Wei","orcid":"https://orcid.org/0000-0002-8200-1845"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiu-Shen Wei","raw_affiliation_strings":["School of Computer Science and Engineering and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8200-1845","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409301","display_name":"Heyang Xu","orcid":"https://orcid.org/0009-0007-3924-2639"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He-Yang Xu","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0007-3924-2639","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101936895","display_name":"Zhiwen Yang","orcid":"https://orcid.org/0000-0002-0416-0576"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwen Yang","raw_affiliation_strings":["Wangxuan Institute of Computer Technology and National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0416-0576","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology and National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100754603","display_name":"Chenlong Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen-Long Duan","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0000-8976-4960","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047811387","display_name":"Yuxin Peng","orcid":"https://orcid.org/0000-0001-7658-3845"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Peng","raw_affiliation_strings":["Wangxuan Institute of Computer Technology and National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7658-3845","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology and National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7155,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.94552263,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"46","issue":"4","first_page":"2091","last_page":"2103"},"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":0.9986000061035156,"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":0.9986000061035156,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9258000254631042,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7797353267669678},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7276322841644287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.642496645450592},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6208932399749756},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5581299066543579},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5024383068084717},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45055699348449707},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4470495879650116},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42799291014671326},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41327595710754395},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.348816454410553},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2257688343524933}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7797353267669678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276322841644287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.642496645450592},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6208932399749756},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5581299066543579},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5024383068084717},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45055699348449707},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4470495879650116},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42799291014671326},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41327595710754395},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.348816454410553},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2257688343524933},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2023.3333528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3333528","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:37971914","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37971914","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4894062418","display_name":null,"funder_award_id":"62272231","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5930747381","display_name":null,"funder_award_id":"NJ2022028","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6078352744","display_name":null,"funder_award_id":"62132001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7056437236","display_name":null,"funder_award_id":"2021YFA1001100","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7427494650","display_name":null,"funder_award_id":"61925201","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W2085443648","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2796346823","https://openalex.org/W2919115771","https://openalex.org/W2981952612","https://openalex.org/W2988916019","https://openalex.org/W3010597097","https://openalex.org/W3012255272","https://openalex.org/W3034312118","https://openalex.org/W3034453888","https://openalex.org/W3034942609","https://openalex.org/W3035531117","https://openalex.org/W3108975329","https://openalex.org/W3111381272","https://openalex.org/W3155876088","https://openalex.org/W3165838961","https://openalex.org/W3176341011","https://openalex.org/W3202483439","https://openalex.org/W3213192039","https://openalex.org/W4212863985","https://openalex.org/W4214562728","https://openalex.org/W4221161877","https://openalex.org/W4287121509","https://openalex.org/W4290830415","https://openalex.org/W4312589581","https://openalex.org/W4312935555","https://openalex.org/W4313010934","https://openalex.org/W4385338545","https://openalex.org/W6638319203","https://openalex.org/W6681588610","https://openalex.org/W6717697761","https://openalex.org/W6730161283","https://openalex.org/W6733814495","https://openalex.org/W6736057607","https://openalex.org/W6738532226","https://openalex.org/W6743661861","https://openalex.org/W6748284727","https://openalex.org/W6752232076","https://openalex.org/W6753311412","https://openalex.org/W6755766585","https://openalex.org/W6758126075","https://openalex.org/W6762818971","https://openalex.org/W6764051988","https://openalex.org/W6767471572","https://openalex.org/W6787972765","https://openalex.org/W6796794720","https://openalex.org/W6803279866","https://openalex.org/W6844056358"],"related_works":["https://openalex.org/W2129767422","https://openalex.org/W3210196349","https://openalex.org/W2950181282","https://openalex.org/W2798287483","https://openalex.org/W2913410650","https://openalex.org/W3204418343","https://openalex.org/W1560624709","https://openalex.org/W1996537998","https://openalex.org/W4398789279","https://openalex.org/W3214142563"],"abstract_inverted_index":{"Semi-Supervised":[0],"Few-Shot":[1],"Learning":[2],"(SSFSL)":[3],"aims":[4],"to":[5,12,32,51,68,182],"train":[6],"a":[7,20,45,106,172],"classifier":[8],"that":[9],"can":[10,89,137],"adapt":[11],"new":[13],"tasks":[14],"using":[15,148],"limited":[16,76,125],"labeled":[17],"data":[18,58],"and":[19,120,166,179],"fixed":[21],"amount":[22],"of":[23,56,118,144],"unlabeled":[24,57,111],"data.":[25],"Various":[26],"sophisticated":[27],"methods":[28,155,178],"have":[29],"been":[30],"proposed":[31],"tackle":[33],"the":[34,70,124],"challenges":[35],"associated":[36],"with":[37],"this":[38,41],"problem.":[39],"In":[40,83],"paper,":[42],"we":[43,103],"present":[44],"simple":[46],"but":[47],"quite":[48],"effective":[49],"approach":[50,88,136,162],"predict":[52],"accurate":[53,92],"negative":[54,93,98,119],"pseudo-labels":[55,67,99,122],"from":[59],"an":[60],"indirect":[61],"learning":[62],"perspective.":[63],"We":[64],"leverage":[65],"these":[66],"augment":[69],"support":[71,126],"set,":[72,127],"which":[73],"is":[74],"typically":[75],"in":[77,113,129,140],"few-shot":[78],"tasks,":[79],"e.g.,":[80],"1-shot":[81],"classification.":[82],"such":[84],"label-constrained":[85],"scenarios,":[86],"our":[87,114,161],"offer":[90],"highly":[91],"pseudo-labels.":[94],"By":[95],"iteratively":[96],"excluding":[97],"one":[100],"by":[101,146],"one,":[102],"ultimately":[104],"derive":[105],"positive":[107,121],"pseudo-label":[108],"for":[109,133],"each":[110],"sample":[112],"approach.":[115],"The":[116],"integration":[117],"complements":[123],"resulting":[128],"significant":[130],"accuracy":[131],"improvements":[132],"SSFSL.":[134],"Our":[135],"be":[138],"implemented":[139],"just":[141],"few":[142],"lines":[143],"code":[145],"only":[147],"off-the-shelf":[149],"operations,":[150],"yet":[151],"it":[152],"outperforms":[153],"state-of-the-art":[154],"on":[156],"four":[157],"benchmark":[158],"datasets.":[159],"Furthermore,":[160],"exhibits":[163],"good":[164],"adaptability":[165],"generalization":[167],"capabilities":[168],"when":[169,180],"used":[170],"as":[171],"plug-and-play":[173],"counterpart":[174],"alongside":[175],"existing":[176],"SSFSL":[177],"extended":[181],"generalized":[183],"linear":[184],"models.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2025-10-10T00:00:00"}