{"id":"https://openalex.org/W3161942977","doi":"https://doi.org/10.1109/icassp39728.2021.9414410","title":"Learning a Sparse Generative Non-Parametric Supervised Autoencoder","display_name":"Learning a Sparse Generative Non-Parametric Supervised Autoencoder","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161942977","doi":"https://doi.org/10.1109/icassp39728.2021.9414410","mag":"3161942977"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5108556119","display_name":"Michel Barlaud","orcid":null},"institutions":[{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Michel Barlaud","raw_affiliation_strings":["Laboratoire I3S CNRS, Cote d\u2019Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France","Laboratoire I3S CNRS, Cote d'Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France"],"affiliations":[{"raw_affiliation_string":"Laboratoire I3S CNRS, Cote d\u2019Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France","institution_ids":["https://openalex.org/I19370010","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Laboratoire I3S CNRS, Cote d'Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France","institution_ids":["https://openalex.org/I19370010","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064260662","display_name":"Fr\u00e9d\u00e9ric Guyard","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Frederic Guyard","raw_affiliation_strings":["Laboratoire I3S CNRS, Cote d\u2019Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France","Laboratoire I3S CNRS, Cote d'Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France"],"affiliations":[{"raw_affiliation_string":"Laboratoire I3S CNRS, Cote d\u2019Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France","institution_ids":["https://openalex.org/I19370010","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Laboratoire I3S CNRS, Cote d'Azur University Sophia Antipolis, France Orange Labs, Sophia Antipolis, France","institution_ids":["https://openalex.org/I19370010","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108556119"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I19370010"],"apc_list":null,"apc_paid":null,"fwci":0.7685,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.73022876,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3315","last_page":"3319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9997000098228455,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9966999888420105,"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/T10862","display_name":"AI in cancer detection","score":0.9746000170707703,"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/autoencoder","display_name":"Autoencoder","score":0.8689754009246826},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.643692672252655},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6218622922897339},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6108294129371643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5953255295753479},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5743124485015869},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4859734773635864},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4691782295703888},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.46365317702293396},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.44505253434181213},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.44460222125053406},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40743955969810486},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3200075030326843},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.2924458980560303},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2798752188682556},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07686564326286316}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8689754009246826},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.643692672252655},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6218622922897339},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6108294129371643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5953255295753479},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5743124485015869},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4859734773635864},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4691782295703888},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46365317702293396},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.44505253434181213},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.44460222125053406},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40743955969810486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3200075030326843},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2924458980560303},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2798752188682556},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07686564326286316},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1523985187","https://openalex.org/W1663973292","https://openalex.org/W1781062201","https://openalex.org/W1871180460","https://openalex.org/W1959608418","https://openalex.org/W1987971958","https://openalex.org/W2009086942","https://openalex.org/W2028510317","https://openalex.org/W2099471712","https://openalex.org/W2102409316","https://openalex.org/W2108501770","https://openalex.org/W2113315423","https://openalex.org/W2180180824","https://openalex.org/W2399306074","https://openalex.org/W2489822048","https://openalex.org/W2556467266","https://openalex.org/W2796335507","https://openalex.org/W2805003733","https://openalex.org/W2919115771","https://openalex.org/W2953915517","https://openalex.org/W2962963232","https://openalex.org/W2963373786","https://openalex.org/W2963813662","https://openalex.org/W2964121744","https://openalex.org/W2970277060","https://openalex.org/W2982127418","https://openalex.org/W3022566213","https://openalex.org/W3113770201","https://openalex.org/W4295177495","https://openalex.org/W4297940714","https://openalex.org/W4320013936","https://openalex.org/W6638241746","https://openalex.org/W6639239318","https://openalex.org/W6640963894","https://openalex.org/W6675401909","https://openalex.org/W6675944832","https://openalex.org/W6718379498","https://openalex.org/W6730333270","https://openalex.org/W6749820799","https://openalex.org/W6751979845","https://openalex.org/W6761601108","https://openalex.org/W6777259998","https://openalex.org/W6787198391"],"related_works":["https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W2937381246","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W4394785709","https://openalex.org/W2978098801","https://openalex.org/W4289105138"],"abstract_inverted_index":{"This":[0],"paper":[1],"concerns":[2],"the":[3,22,41,50,58,63,76,79,85,94,98,110,114,141,145,149,159,165,171],"supervised":[4,16],"generative":[5],"non":[6,15],"parametric":[7,59,99],"autoencoder.":[8],"Classical":[9],"methods":[10],"are":[11],"based":[12],"on":[13],"variational":[14],"autoencoders":[17,20],"(VAE).":[18],"Variational":[19],"encourage":[21],"latent":[23,64,80,86,115],"space":[24,65,87],"to":[25,36,45,69,88,112,157],"fit":[26,89],"a":[27,31,71,90,105,120,133,154],"prior":[28,100],"distribution,":[29],"like":[30],"Gaussian.":[32],"However,":[33],"they":[34],"tend":[35],"draw":[37],"stronger":[38],"assumptions":[39],"for":[40],"data,":[42],"often":[43],"leading":[44],"higher":[46],"asymptotic":[47],"bias":[48],"when":[49],"model":[51],"is":[52],"wrong.In":[53],"this":[54],"paper,":[55],"we":[56,67,118,130,152],"relax":[57],"distribution":[60,74,91],"assumption":[61],"in":[62,78],"and":[66,125,147,176],"propose":[68,153],"learn":[70],"non-parametric":[72],"data":[73],"of":[75,97,143,167],"clusters":[77],"space.":[81,116],"The":[82],"network":[83,106,146],"encourages":[84],"learned":[92],"with":[93,161],"labels":[95,111],"instead":[96],"assumptions.":[101],"We":[102,163],"have":[103,131],"built":[104],"architecture":[107],"that":[108],"uses":[109],"compute":[113],"Thus":[117],"define":[119],"global":[121],"criterion":[122,160],"combining":[123],"classification":[124],"reconstruction":[126],"loss.":[127],"In":[128],"addition,":[129],"proposed":[132],"\u2113":[134],"<inf":[135],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[136],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1,1</inf>":[137],"regularization":[138],"which":[139],"has":[140],"advantage":[142],"sparsifying":[144],"improving":[148],"clustering.":[150],"Finally":[151],"tailored":[155],"algorithm":[156],"minimize":[158],"constraint.":[162],"demonstrate":[164],"effectiveness":[166],"our":[168],"method":[169],"using":[170],"popular":[172],"image":[173],"dataset":[174],"MNIST":[175],"two":[177],"biological":[178],"datasets.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}