{"id":"https://openalex.org/W3208933490","doi":"https://doi.org/10.1587/transinf.2021edp7046","title":"Flexible Bayesian Inference by Weight Transfer for Robust Deep Neural Networks","display_name":"Flexible Bayesian Inference by Weight Transfer for Robust Deep Neural Networks","publication_year":2021,"publication_date":"2021-10-31","ids":{"openalex":"https://openalex.org/W3208933490","doi":"https://doi.org/10.1587/transinf.2021edp7046","mag":"3208933490"},"language":"en","primary_location":{"id":"doi:10.1587/transinf.2021edp7046","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2021edp7046","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E104.D/11/E104.D_2021EDP7046/_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/E104.D/11/E104.D_2021EDP7046/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046136958","display_name":"Thi Thu Thao Khong","orcid":null},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Thi Thu Thao KHONG","raw_affiliation_strings":["Nara Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology","institution_ids":["https://openalex.org/I75917431"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042516424","display_name":"Takashi Nakada","orcid":"https://orcid.org/0000-0001-7512-2545"},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takashi NAKADA","raw_affiliation_strings":["Nara Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology","institution_ids":["https://openalex.org/I75917431"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074853381","display_name":"Yasuhiko Nakashima","orcid":"https://orcid.org/0000-0002-9457-5061"},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasuhiko NAKASHIMA","raw_affiliation_strings":["Nara Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology","institution_ids":["https://openalex.org/I75917431"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I75917431"],"apc_list":null,"apc_paid":null,"fwci":0.1303,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58977073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"E104.D","issue":"11","first_page":"1981","last_page":"1991"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine 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/T11689","display_name":"Adversarial Robustness in Machine 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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9811999797821045,"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/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.942799985408783,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8523370623588562},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7354885935783386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7219520807266235},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6784592866897583},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6749880313873291},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6648285388946533},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6306062936782837},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5532033443450928},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5390187501907349},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4521414041519165},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44129160046577454},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4348596930503845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8523370623588562},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7354885935783386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7219520807266235},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6784592866897583},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6749880313873291},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6648285388946533},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6306062936782837},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5532033443450928},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5390187501907349},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4521414041519165},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44129160046577454},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4348596930503845},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1587/transinf.2021edp7046","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2021edp7046","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E104.D/11/E104.D_2021EDP7046/_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.2021edp7046","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2021edp7046","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E104.D/11/E104.D_2021EDP7046/_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":[{"display_name":"Industry, innovation and infrastructure","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3208933490.pdf","grobid_xml":"https://content.openalex.org/works/W3208933490.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W71499226","https://openalex.org/W601603264","https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2180612164","https://openalex.org/W2194775991","https://openalex.org/W2460937040","https://openalex.org/W2552767274","https://openalex.org/W2603766943","https://openalex.org/W2620038827","https://openalex.org/W2640329709","https://openalex.org/W2774644650","https://openalex.org/W2787733970","https://openalex.org/W2804078698","https://openalex.org/W2891698621","https://openalex.org/W2895033754","https://openalex.org/W2916286792","https://openalex.org/W2946948417","https://openalex.org/W2949718784","https://openalex.org/W2951266961","https://openalex.org/W2962710014","https://openalex.org/W2962872506","https://openalex.org/W2963495494","https://openalex.org/W2963564844","https://openalex.org/W2963626858","https://openalex.org/W2963857521","https://openalex.org/W2964082701","https://openalex.org/W2965596382","https://openalex.org/W2996564870","https://openalex.org/W3034885317","https://openalex.org/W3035743198","https://openalex.org/W3048203754","https://openalex.org/W3098989436","https://openalex.org/W3135034047","https://openalex.org/W3151299405"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Adversarial":[0],"attacks":[1,203,283],"are":[2,239],"viewed":[3],"as":[4,30,162],"a":[5,14,41,190,199,205],"danger":[6],"to":[7,45,64,68,73,91,108,138,174,176,195],"Deep":[8],"Neural":[9,111,154],"Networks":[10,112,155,164],"(DNNs),":[11],"which":[12,238,294],"reveal":[13],"weakness":[15],"of":[16,98,105,192,201,215,261],"deep":[17],"learning":[18],"models":[19],"in":[20,226],"security-critical":[21],"applications.":[22],"Recent":[23],"findings":[24],"have":[25],"been":[26],"presented":[27],"adversarial":[28,38,69,76,127,140,202,272,300],"training":[29,39],"an":[31,96],"outstanding":[32],"defense":[33],"method":[34,130,264],"against":[35,198],"adversaries.":[36],"Nonetheless,":[37],"is":[40,52,80,131,171,250,295],"challenge":[42],"with":[43,125,183,219,241,265],"respect":[44],"big":[46],"datasets":[47],"and":[48,114,181,229,246,271,281,297,306],"large":[49],"networks.":[50],"It":[51],"believed":[53],"that,":[54],"unless":[55],"making":[56],"DNN":[57,193],"architectures":[58,194],"larger,":[59],"DNNs":[60,107],"would":[61],"be":[62],"hard":[63],"strengthen":[65],"the":[66,88,93,277,290],"robustness":[67,278],"examples.":[70],"In":[71,123,208],"order":[72],"avoid":[74],"iteratively":[75],"training,":[77,128],"our":[78,129,187,224,262],"algorithm":[79,188],"Bayes":[81],"without":[82,119,284],"Bayesian":[83,110,117,121],"Learning":[84],"(BwoBL)":[85],"that":[86,166],"performs":[87],"ensemble":[89],"inference":[90,118],"improve":[92],"robustness.":[94,141],"As":[95],"application":[97],"transfer":[99],"learning,":[100],"we":[101],"use":[102],"learned":[103],"parameters":[104],"pretrained":[106,149,243,266],"build":[109],"(BNNs)":[113],"focus":[115],"on":[116,204,222,236,252,268,299],"costing":[120],"learning.":[122],"comparison":[124],"no":[126],"more":[132],"robust":[133],"than":[134],"activation":[135],"functions":[136],"designed":[137],"enhance":[139],"Moreover,":[142],"BwoBL":[143,170],"can":[144],"easily":[145],"integrate":[146],"into":[147],"any":[148],"DNN,":[150],"not":[151],"only":[152],"Convolutional":[153],"(CNNs)":[156],"but":[157],"also":[158,172],"other":[159],"DNNs,":[160],"such":[161],"Self-Attention":[163],"(SANs)":[165],"outperform":[167],"convolutional":[168],"counterparts.":[169],"convenient":[173],"apply":[175],"scaling":[177],"networks,":[178],"e.g.,":[179],"ResNet":[180],"EfficientNet,":[182],"better":[184],"performance.":[185],"Especially,":[186],"employs":[189],"variety":[191],"construct":[196],"BNNs":[197],"diversity":[200],"large-scale":[206],"dataset.":[207],"particular,":[209],"under":[210],"l\u221e":[211],"norm":[212,256],"PGD":[213,280,305],"attack":[214],"pixel":[216],"perturbation":[217],"\u03b5=4/255":[218],"100":[220],"iterations":[221],"ImageNet,":[223],"proposal":[225],"ResNets,":[227,244],"SANs,":[228,245],"EfficientNets":[230,267],"increase":[231],"by":[232,303],"58.18%":[233],"top-5":[234,292],"accuracy":[235],"average,":[237],"combined":[240],"naturally":[242],"EfficientNets.":[247],"This":[248],"enhancement":[249],"62.26%":[251],"average":[253],"below":[254],"l2":[255],"C&W":[257,282,307],"attack.":[258],"The":[259],"combination":[260],"proposed":[263],"both":[269],"natural":[270],"images":[273],"(EfficientNet-ADV)":[274],"drastically":[275],"boosts":[276],"resisting":[279],"additional":[285],"training.":[286],"Our":[287],"EfficientNet-ADV-B7":[288],"achieves":[289],"cutting-edge":[291],"accuracy,":[293],"92.14%":[296],"94.20%":[298],"ImageNet":[301],"generated":[302],"powerful":[304],"attacks,":[308],"respectively.":[309]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}
