{"id":"https://openalex.org/W2025768430","doi":"https://doi.org/10.1145/1390156.1390294","title":"Extracting and composing robust features with denoising autoencoders","display_name":"Extracting and composing robust features with denoising autoencoders","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2025768430","doi":"https://doi.org/10.1145/1390156.1390294","mag":"2025768430"},"language":"en","primary_location":{"id":"doi:10.1145/1390156.1390294","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1390156.1390294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th international conference on Machine learning - ICML '08","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/A5103958886","display_name":"Pascal Vincent","orcid":null},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Pascal Vincent","raw_affiliation_strings":["Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019862529","display_name":"Hugo Larochelle","orcid":null},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hugo Larochelle","raw_affiliation_strings":["Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086198262","display_name":"Yoshua Bengio","orcid":"https://orcid.org/0000-0002-9322-3515"},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yoshua Bengio","raw_affiliation_strings":["Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006599919","display_name":"Pierre-Antoine Manzagol","orcid":"https://orcid.org/0009-0009-5047-6369"},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Pierre-Antoine Manzagol","raw_affiliation_strings":["Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al, Montral, Qubec, Canada","institution_ids":["https://openalex.org/I70931966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70931966"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7377,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1096","last_page":"1103"},"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":0.8827999830245972,"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":0.8827999830245972,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.034299999475479126,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.01679999940097332,"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":0.7751109600067139},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.769629180431366},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6797425746917725},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6351353526115417},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5792490839958191},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5184111595153809},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5156675577163696},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5133165717124939},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.50704026222229},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.48599013686180115},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.484391987323761},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4647250771522522},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45509761571884155},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.41488659381866455}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7751109600067139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.769629180431366},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6797425746917725},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6351353526115417},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5792490839958191},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5184111595153809},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5156675577163696},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5133165717124939},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.50704026222229},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.48599013686180115},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.484391987323761},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4647250771522522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45509761571884155},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.41488659381866455},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1145/1390156.1390294","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1390156.1390294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th international conference on Machine learning - ICML '08","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.141.2238","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.2238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.149.8111","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.8111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://icml2008.cs.helsinki.fi/papers/592.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.298.4083","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.4083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.iro.umontreal.ca/~vincentp/Publications/vincent_icml_2008.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.329.7817","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.7817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.toronto.edu/~larocheh/publications/denoising_autoencoders_tr1316.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1526741802","https://openalex.org/W1586881595","https://openalex.org/W1994197834","https://openalex.org/W2072128103","https://openalex.org/W2100495367","https://openalex.org/W2108665656","https://openalex.org/W2110798204","https://openalex.org/W2111406701","https://openalex.org/W2112812053","https://openalex.org/W2128076038","https://openalex.org/W2128084896","https://openalex.org/W2131686571","https://openalex.org/W2133257461","https://openalex.org/W2136922672","https://openalex.org/W2149384588","https://openalex.org/W2153014441","https://openalex.org/W2153663612","https://openalex.org/W2172174689","https://openalex.org/W2176028050","https://openalex.org/W2609208295","https://openalex.org/W2613634265","https://openalex.org/W3207342693"],"related_works":["https://openalex.org/W4396941953","https://openalex.org/W2987280934","https://openalex.org/W3119773509","https://openalex.org/W3174759195","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W3167013339"],"abstract_inverted_index":{"Previous":[0],"work":[1],"has":[2],"shown":[3],"that":[4,23],"the":[5,46,50,58,103,108],"difficulties":[6],"in":[7],"learning":[8,21,40,88],"deep":[9,78],"generative":[10,96],"or":[11,93],"discriminative":[12],"models":[13],"can":[14,63,73,82],"be":[15,64,74,83],"overcome":[16],"by":[17],"an":[18],"initial":[19],"unsupervised":[20,39],"step":[22],"maps":[24],"inputs":[25],"to":[26,54,66,76],"useful":[27],"intermediate":[28],"representations.":[29],"We":[30],"introduce":[31],"and":[32,69,89],"motivate":[33],"a":[34,42,86,95,113],"new":[35],"training":[36],"principle":[37],"for":[38],"of":[41,48,57,106,110],"representation":[43],"based":[44],"on":[45,112],"idea":[47],"making":[49],"learned":[51],"representations":[52],"robust":[53],"partial":[55],"corruption":[56],"input":[59,109],"pattern.":[60],"This":[61],"approach":[62],"used":[65],"train":[67],"autoencoders,":[68],"these":[70],"denoising":[71],"autoencoders":[72,111],"stacked":[75],"initialize":[77],"architectures.":[79],"The":[80],"algorithm":[81],"motivated":[84],"from":[85,94],"manifold":[87],"information":[90],"theoretic":[91],"perspective":[92],"model":[97],"perspective.":[98],"Comparative":[99],"experiments":[100],"clearly":[101],"show":[102],"surprising":[104],"advantage":[105],"corrupting":[107],"pattern":[114],"classification":[115],"benchmark":[116],"suite.":[117],"1.":[118]},"counts_by_year":[{"year":2026,"cited_by_count":147},{"year":2025,"cited_by_count":497},{"year":2024,"cited_by_count":563},{"year":2023,"cited_by_count":615},{"year":2022,"cited_by_count":647},{"year":2021,"cited_by_count":866},{"year":2020,"cited_by_count":903},{"year":2019,"cited_by_count":842},{"year":2018,"cited_by_count":648},{"year":2017,"cited_by_count":524},{"year":2016,"cited_by_count":390},{"year":2015,"cited_by_count":315},{"year":2014,"cited_by_count":188},{"year":2013,"cited_by_count":110},{"year":2012,"cited_by_count":52}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}