{"id":"https://openalex.org/W4312516353","doi":"https://doi.org/10.1109/icpr56361.2022.9956566","title":"Prototype Augmentation with Dummy Samples","display_name":"Prototype Augmentation with Dummy Samples","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312516353","doi":"https://doi.org/10.1109/icpr56361.2022.9956566"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956566","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5071183898","display_name":"Hong\u2010Ren Yu","orcid":"https://orcid.org/0000-0003-1242-8760"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Yu","raw_affiliation_strings":["Soochow University,School of Computer Science and Technology,Suzhou,China","School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow University,School of Computer Science and Technology,Suzhou,China","institution_ids":["https://openalex.org/I3923682"]},{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013713640","display_name":"Fanzhang Li","orcid":"https://orcid.org/0000-0003-4318-3081"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanzhang Li","raw_affiliation_strings":["Soochow University,School of Computer Science and Technology,Suzhou,China","School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow University,School of Computer Science and Technology,Suzhou,China","institution_ids":["https://openalex.org/I3923682"]},{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5052","last_page":"5059"},"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":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9907000064849854,"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":0.9850999712944031,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8031100034713745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6161911487579346},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5145613551139832},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4967206120491028},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46945351362228394},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.46851038932800293},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4565439224243164},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.43767470121383667},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4233309030532837},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42216819524765015},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37675076723098755},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.322909951210022},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14117810130119324}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8031100034713745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161911487579346},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5145613551139832},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4967206120491028},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46945351362228394},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.46851038932800293},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4565439224243164},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.43767470121383667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4233309030532837},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42216819524765015},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37675076723098755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.322909951210022},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14117810130119324},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956566","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":73,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W2097117768","https://openalex.org/W2108598243","https://openalex.org/W2138011018","https://openalex.org/W2145680191","https://openalex.org/W2163605009","https://openalex.org/W2188365844","https://openalex.org/W2194775991","https://openalex.org/W2470142083","https://openalex.org/W2601450892","https://openalex.org/W2604763608","https://openalex.org/W2606712314","https://openalex.org/W2742093937","https://openalex.org/W2753160622","https://openalex.org/W2787501667","https://openalex.org/W2803832867","https://openalex.org/W2808498263","https://openalex.org/W2887997457","https://openalex.org/W2892122929","https://openalex.org/W2949736877","https://openalex.org/W2949879676","https://openalex.org/W2950763986","https://openalex.org/W2962723986","https://openalex.org/W2962895018","https://openalex.org/W2963070905","https://openalex.org/W2963226019","https://openalex.org/W2963341924","https://openalex.org/W2963943197","https://openalex.org/W2964105864","https://openalex.org/W2964112702","https://openalex.org/W2990216037","https://openalex.org/W2992308087","https://openalex.org/W3001411605","https://openalex.org/W3012209922","https://openalex.org/W3015113763","https://openalex.org/W3021864183","https://openalex.org/W3034251792","https://openalex.org/W3034710639","https://openalex.org/W3035143213","https://openalex.org/W3092742756","https://openalex.org/W3122168634","https://openalex.org/W3159907343","https://openalex.org/W3182874523","https://openalex.org/W3189329097","https://openalex.org/W4293412117","https://openalex.org/W6640963894","https://openalex.org/W6684191040","https://openalex.org/W6687045409","https://openalex.org/W6717697761","https://openalex.org/W6718140377","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6736562241","https://openalex.org/W6742288159","https://openalex.org/W6743661861","https://openalex.org/W6746260573","https://openalex.org/W6747943641","https://openalex.org/W6749101112","https://openalex.org/W6751281049","https://openalex.org/W6752051073","https://openalex.org/W6752940074","https://openalex.org/W6753311412","https://openalex.org/W6754278344","https://openalex.org/W6754673040","https://openalex.org/W6755095635","https://openalex.org/W6762226699","https://openalex.org/W6763315676","https://openalex.org/W6771301040","https://openalex.org/W6772329248","https://openalex.org/W6775093496","https://openalex.org/W6777097145","https://openalex.org/W6784057640","https://openalex.org/W6789479168"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W4231775656","https://openalex.org/W2561132942","https://openalex.org/W2046435967","https://openalex.org/W3155418658","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Meta-learning":[0],"is":[1,62,84,149],"proven":[2],"to":[3,12,36,50,78,91,114,137,155,193],"be":[4],"powerful":[5],"for":[6,46],"helping":[7],"models":[8],"make":[9],"quick":[10],"adaptations":[11],"new":[13],"tasks.":[14],"However,":[15],"the":[16,22,79,93,98,142,152,194],"lack":[17,102],"of":[18,57,101,103],"data":[19,26,48,58,146],"severely":[20],"constrains":[21],"further":[23],"improvement.":[24],"As":[25],"augmentation":[27,49,59,147],"has":[28],"been":[29],"an":[30],"effective":[31],"and":[32,60,72,96,118,183,186],"commonly":[33],"used":[34],"approach":[35],"reach":[37],"state-of-art":[38],"performance":[39],"in":[40,151,178],"image":[41,181],"classification":[42,182],"tasks,":[43],"different":[44,64],"strategies":[45],"applying":[47],"meta-learning":[51,61],"have":[52,108,112],"emerged.":[53],"One":[54],"common":[55],"combination":[56],"performing":[63],"transformations":[65],"on":[66],"images(e.g.":[67],"horizontal":[68],"flip,":[69],"color":[70],"jitter,":[71],"random":[73],"crop)":[74],"before":[75],"feeding":[76],"them":[77],"feature":[80,153],"extractor.":[81],"Another":[82],"way":[83],"using":[85,131],"generative":[86],"models,":[87],"such":[88],"as":[89,167],"GAN,":[90],"expand":[92],"available":[94],"dataset":[95],"alleviate":[97],"negative":[99],"effects":[100],"data.":[104],"These":[105],"methods":[106],"either":[107],"limited":[109],"boosts":[110],"or":[111],"difficulties":[113],"converge":[115],"during":[116],"training":[117],"require":[119],"significant":[120,189],"extra":[121],"computation.":[122],"In":[123],"this":[124],"paper,":[125],"we":[126,163,187],"propose":[127],"a":[128,132],"novel":[129],"method":[130,148,166],"modified":[133],"Variational":[134],"Auto":[135],"Encoder(VAE)":[136],"generate":[138],"dummy":[139],"samples":[140],"from":[141],"support":[143],"set.":[144],"Our":[145],"performed":[150],"space":[154],"reduce":[156],"computation":[157],"steps.":[158],"Combined":[159],"with":[160,170,191],"prototypical":[161],"networks":[162],"call":[164],"our":[165],"Prototype":[168],"Augmentation":[169],"Dummy":[171],"Samples(PADS).":[172],"Experiments":[173],"are":[174],"carried":[175],"out":[176],"both":[177],"standard":[179],"few-shot":[180],"cross-domain":[184],"scenarios":[185],"achieved":[188],"improvement":[190],"comparison":[192],"baseline.":[195]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
