{"id":"https://openalex.org/W3210764384","doi":"https://doi.org/10.48550/arxiv.2110.13386","title":"Self-Denoising Neural Networks for Few Shot Learning","display_name":"Self-Denoising Neural Networks for Few Shot Learning","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3210764384","doi":"https://doi.org/10.48550/arxiv.2110.13386","mag":"3210764384"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2110.13386","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.13386","pdf_url":"https://arxiv.org/pdf/2110.13386","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2110.13386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024568738","display_name":"Steven L. Schwarcz","orcid":"https://orcid.org/0000-0001-6252-7025","observed_orcids":["https://orcid.org/0000-0001-6252-7025"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Schwarcz, Steven","raw_affiliation_strings":["University of Maryland , College Park College Park , MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland , College Park College Park , MD","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088236196","display_name":"Sai Saketh Rambhatla","orcid":"https://orcid.org/0009-0002-2135-8311","observed_orcids":["https://orcid.org/0009-0002-2135-8311"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rambhatla, Sai Saketh","raw_affiliation_strings":["University of Maryland , College Park College Park , MD","University of Maryland - College Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland , College Park College Park , MD","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"University of Maryland - College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102762707","display_name":"Rama Chellappa","orcid":"https://orcid.org/0000-0002-7638-1650","observed_orcids":["https://orcid.org/0000-0002-7638-1650"]},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chellappa, Rama","raw_affiliation_strings":["Johns Hopkins University Baltimore , MD","Johns Hopkins University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University Baltimore , MD","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":1.0,"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":1.0,"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":0.0,"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":0.0,"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/few-shot-learning","display_name":"few-shot learning","score":0.9330000281333923},{"id":"https://openalex.org/keywords/denoising-autoencoder","display_name":"denoising autoencoder","score":0.9900000095367432},{"id":"https://openalex.org/keywords/noise-injection","display_name":"noise injection","score":0.6589999794960022},{"id":"https://openalex.org/keywords/noise-robustness","display_name":"noise robustness","score":0.6830000281333923},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8009999990463257},{"id":"https://openalex.org/keywords/miniimagenet","display_name":"miniImageNet","score":0.6830000281333923},{"id":"https://openalex.org/keywords/tieredimagenet","display_name":"tieredImageNet","score":0.9210000038146973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7733874320983887},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6825904846191406},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6289174556732178},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5998585224151611},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5329626202583313},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48011255264282227},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4777630865573883},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.4742625951766968},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4608911871910095},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4574536383152008},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4460003077983856},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4441731870174408},{"id":"https://openalex.org/C2780343955","wikidata":"https://www.wikidata.org/wiki/Q333173","display_name":"Surprise","level":2,"score":0.43755030632019043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4036196768283844},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35275986790657043},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07457628846168518},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2110.13386","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.13386","pdf_url":"https://arxiv.org/pdf/2110.13386","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2110.13386","doi":"https://doi.org/10.48550/arxiv.2110.13386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2110.13386","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2110.13386","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.13386","pdf_url":"https://arxiv.org/pdf/2110.13386","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2181426379","display_name":null,"funder_award_id":"D17PC00345","funder_id":"https://openalex.org/F4320333051","funder_display_name":"Intelligence Advanced Research Projects Activity"},{"id":"https://openalex.org/G2343490607","display_name":null,"funder_award_id":"D17PC00345","funder_id":"https://openalex.org/F4320312530","funder_display_name":"Office of the Director of National Intelligence"}],"funders":[{"id":"https://openalex.org/F4320312530","display_name":"Office of the Director of National Intelligence","ror":"https://ror.org/01v3fsc55"},{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3210764384.pdf","grobid_xml":"https://content.openalex.org/works/W3210764384.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W2025768430","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2145094598","https://openalex.org/W2194321275","https://openalex.org/W2194775991","https://openalex.org/W2562986790","https://openalex.org/W2601450892","https://openalex.org/W2614634292","https://openalex.org/W2619082050","https://openalex.org/W2625674597","https://openalex.org/W2770468159","https://openalex.org/W2796346823","https://openalex.org/W2798836702","https://openalex.org/W2803672301","https://openalex.org/W2803895400","https://openalex.org/W2901372335","https://openalex.org/W2911906319","https://openalex.org/W2931116403","https://openalex.org/W2934413201","https://openalex.org/W2943052531","https://openalex.org/W2944737479","https://openalex.org/W2950996604","https://openalex.org/W2963341924","https://openalex.org/W2963845150","https://openalex.org/W2964105864","https://openalex.org/W2964137095","https://openalex.org/W2970105755","https://openalex.org/W2970971581","https://openalex.org/W2971715690","https://openalex.org/W2988501586","https://openalex.org/W2996623013","https://openalex.org/W3027153263","https://openalex.org/W3091905774","https://openalex.org/W3107376673","https://openalex.org/W3108975329","https://openalex.org/W3109083691"],"related_works":["https://openalex.org/W7103140204","https://openalex.org/W3210329082","https://openalex.org/W4321770459","https://openalex.org/W7168026564","https://openalex.org/W4387766320","https://openalex.org/W2950996604","https://openalex.org/W4411245027","https://openalex.org/W4319943406","https://openalex.org/W4409427032","https://openalex.org/W4293683934"],"abstract_inverted_index":{"In":[0],"this":[1,74],"paper,":[2],"we":[3,50,80,156],"introduce":[4],"a":[5,16,52,82,102,141],"new":[6,53],"architecture":[7,66,127],"for":[8,119],"few":[9,21,120,134,162],"shot":[10,121,135,163],"learning,":[11],"the":[12,30,46,125,129,149,152,166,177],"task":[13,167],"of":[14,33,62,143,151,168],"teaching":[15],"neural":[17,65,95],"network":[18],"from":[19],"as":[20,22,101],"one":[23],"or":[24],"five":[25],"labeled":[26],"examples.":[27],"Inspired":[28],"by":[29],"theoretical":[31],"results":[32],"Alaine":[34],"et":[35],"al":[36],"that":[37,56,113,158],"Denoising":[38],"Autoencoders":[39],"refine":[40],"features":[41],"to":[42,45,70,73,91,104,146],"lie":[43],"closer":[44],"true":[47],"data":[48],"manifold,":[49],"present":[51],"training":[54],"scheme":[55],"adds":[57],"noise":[58],"at":[59],"multiple":[60],"stages":[61],"an":[63],"existing":[64,106],"while":[67],"simultaneously":[68],"learning":[69,108,136],"be":[71,88,99],"robust":[72],"added":[75],"noise.":[76],"This":[77],"architecture,":[78],"which":[79],"call":[81],"Self-Denoising":[83],"Neural":[84],"Network":[85],"(SDNN),":[86],"can":[87,98],"applied":[89],"easily":[90],"most":[92],"modern":[93],"convolutional":[94],"architectures,":[96],"and":[97,132],"used":[100],"supplement":[103],"many":[105],"few-shot":[107],"techniques.":[109],"We":[110,138],"empirically":[111,147],"show":[112,157],"SDNNs":[114,159],"out-perform":[115],"previous":[116],"state-of-the-art":[117],"methods":[118],"image":[122],"recognition":[123],"using":[124,174],"Wide-ResNet":[126],"on":[128,165,176],"\\textit{mini}ImageNet,":[130],"tiered-ImageNet,":[131],"CIFAR-FS":[133],"datasets.":[137],"also":[139],"perform":[140],"series":[142],"ablation":[144],"experiments":[145,175],"justify":[148],"construction":[150],"SDNN":[153],"architecture.":[154],"Finally,":[155],"even":[160],"improve":[161],"performance":[164],"human":[169],"action":[170],"detection":[171],"in":[172],"video":[173],"ActEV":[178],"SDL":[179],"Surprise":[180],"Activities":[181],"challenge.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-11-08T00:00:00"}