{"links":{},"id":187230,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00187230","sets":["6164:6165:6462:9463"]},"path":["9463"],"owner":"11","recid":"187230","title":["深層学習におけるAdversarial Trainingによる副作用とその緩和策"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-16"},"_buckets":{"deposit":"76cbf08a-3e3e-4a20-8d83-b7d8c8e8ab39"},"_deposit":{"id":"187230","pid":{"type":"depid","value":"187230","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"深層学習におけるAdversarial Trainingによる副作用とその緩和策","author_link":["422756","422758","422754","422753","422755","422757"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習におけるAdversarial Trainingによる副作用とその緩和策"},{"subitem_title":"Negative Side Effect of Adversarial Training in Deep Learning and Its Mitigation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"機械学習，ニューラルネットワーク，Adversarial Examples，Adversarial Training","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2017-10-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学生産技術研究所"},{"subitem_text_value":"産業技術総合研究所"},{"subitem_text_value":"東京大学生産技術研究所"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Institute of Industrial Science, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology (AIST)","subitem_text_language":"en"},{"subitem_text_value":"Institute of Industrial Science, The University of Tokyo","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/187230/files/IPSJCSS2017055.pdf","label":"IPSJCSS2017055.pdf"},"date":[{"dateType":"Available","dateValue":"2019-10-16"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2017055.pdf","filesize":[{"value":"1.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5e639b80-8125-4407-be7d-deab093a0904","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"先崎, 佑弥"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大畑, 幸矢"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松浦, 幹太"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuya, Senzaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Satsuya, Ohata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kanta, Matsuura","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_18_relation_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_select":"NCID","subitem_relation_type_id_text":"ISSN　1882-0840"}}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"畳み込みニューラルネットワーク（Convolutional Neural Network, CNN）はその高い精度を理由に注目を集めているが，入力データに微小な改変を加えることでCNNによる認識を大きく誤らせることが可能な敵対的入力の存在が報告されている．この問題への対策として，敵対的入力を学習に活用するAdversarial Trainingと呼ばれる手法が提案されている．本稿では，CNNに対しこの手法を適用すると（本来高い精度で識別できるはずの）ランダムノイズが乗ったデータの識別率が大きく減少してしまうという問題を指摘する．また，その問題を解決するためにランダムノイズを付加した画像も学習に使用する手法を提案し，実験により提案手法の有用性を実証する．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2017論文集"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2017-10-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:53:55.093238+00:00","updated":"2025-01-20T02:23:11.370870+00:00"}