{"id":"https://openalex.org/W2727375987","doi":"https://doi.org/10.1587/transinf.2016edp7468","title":"Effect of Additive Noise for Multi-Layered Perceptron with AutoEncoders","display_name":"Effect of Additive Noise for Multi-Layered Perceptron with AutoEncoders","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2727375987","doi":"https://doi.org/10.1587/transinf.2016edp7468","mag":"2727375987"},"language":"en","primary_location":{"id":"doi:10.1587/transinf.2016edp7468","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2016edp7468","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E100.D/7/E100.D_2016EDP7468/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/transinf/E100.D/7/E100.D_2016EDP7468/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066807814","display_name":"Motaz Sabri","orcid":"https://orcid.org/0000-0002-6560-1179"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Motaz SABRI","raw_affiliation_strings":["Hiroshima University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hiroshima University","institution_ids":["https://openalex.org/I113306721"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086456461","display_name":"Takio Kurita","orcid":"https://orcid.org/0000-0003-3982-6750"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takio KURITA","raw_affiliation_strings":["Hiroshima University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hiroshima University","institution_ids":["https://openalex.org/I113306721"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I113306721"],"apc_list":null,"apc_paid":null,"fwci":1.1963,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.8438859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":"E100.D","issue":"7","first_page":"1494","last_page":"1504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9998999834060669,"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/T10320","display_name":"Neural Networks and Applications","score":0.9998999834060669,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9915000200271606,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/mnist-database","display_name":"MNIST database","score":0.8905225992202759},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8103310465812683},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.7455268502235413},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.711359441280365},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6549406051635742},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6367294788360596},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6154131293296814},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5855175852775574},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5107161998748779},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.50838702917099},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4448883533477783},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4145812392234802},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1280776858329773}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8905225992202759},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8103310465812683},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.7455268502235413},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.711359441280365},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6549406051635742},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6367294788360596},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6154131293296814},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5855175852775574},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5107161998748779},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.50838702917099},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4448883533477783},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4145812392234802},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1280776858329773},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1587/transinf.2016edp7468","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2016edp7468","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E100.D/7/E100.D_2016EDP7468/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1587/transinf.2016edp7468","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2016edp7468","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E100.D/7/E100.D_2016EDP7468/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.46000000834465027,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G7142742633","display_name":"A study on acquisition of intermediate representations using context and top-down information for image recognition","funder_award_id":"16K00239","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G961838086","display_name":null,"funder_award_id":"16H01430","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2727375987.pdf","grobid_xml":"https://content.openalex.org/works/W2727375987.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1519506695","https://openalex.org/W1560979452","https://openalex.org/W1613249581","https://openalex.org/W1652178237","https://openalex.org/W1855879034","https://openalex.org/W1904365287","https://openalex.org/W1973895663","https://openalex.org/W1976320788","https://openalex.org/W2022897209","https://openalex.org/W2025768430","https://openalex.org/W2030214786","https://openalex.org/W2030877883","https://openalex.org/W2111388763","https://openalex.org/W2112117185","https://openalex.org/W2124136621","https://openalex.org/W2187089797","https://openalex.org/W2336750633","https://openalex.org/W2371629303","https://openalex.org/W2471933213","https://openalex.org/W2964121744"],"related_works":["https://openalex.org/W1952005211","https://openalex.org/W2065370636","https://openalex.org/W2076543106","https://openalex.org/W1597533973","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W2523437662","https://openalex.org/W4387048144","https://openalex.org/W2492135063"],"abstract_inverted_index":{"This":[0,82,101],"paper":[1],"investigates":[2],"the":[3,45,54,58,63,87,91,99],"effect":[4],"of":[5,11,23,29,41,71,90],"noises":[6,36],"added":[7,38],"to":[8,14,39,75,85],"hidden":[9,30,42,60,72],"units":[10,31,43,61,73],"AutoEncoders":[12],"linked":[13],"multilayer":[15],"perceptrons.":[16],"It":[17,49],"is":[18,50,83],"shown":[19,52],"that":[20,53,56],"internal":[21],"representation":[22],"learned":[24],"features":[25],"emerges":[26],"and":[27,69],"sparsity":[28],"increases":[32],"when":[33],"independent":[34],"Gaussian":[35],"are":[37],"inputs":[40],"during":[44],"deep":[46,114],"network":[47,92,102,116],"training.":[48],"also":[51],"weights":[55],"connect":[57],"contaminated":[59],"with":[62],"next":[64],"layer":[65],"have":[66],"smaller":[67],"values":[68],"outputs":[70],"tend":[74],"be":[76],"more":[77],"definite":[78],"(0":[79],"or":[80],"1).":[81],"expected":[84],"improve":[86],"generalization":[88],"ability":[89],"through":[93],"this":[94],"automatic":[95],"structuration":[96,103],"by":[97,106],"adding":[98],"noises.":[100],"was":[104],"confirmed":[105],"experiments":[107],"for":[108],"MNIST":[109],"digits":[110],"classification":[111],"via":[112],"a":[113],"neural":[115],"model.":[117]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-13T07:31:44.756512","created_date":"2025-10-10T00:00:00"}
