{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216954","sets":["1164:4619:10826:10881"]},"path":["10881"],"owner":"44499","recid":"216954","title":["[サーベイ論文] Adversarial Training"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-03-03"},"_buckets":{"deposit":"96a14521-cf01-4030-a04d-cfd07b6fab28"},"_deposit":{"id":"216954","pid":{"type":"depid","value":"216954","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"[サーベイ論文] Adversarial Training","author_link":["561103","561107","561106","561102","561104","561108","561105","561109"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"[サーベイ論文] Adversarial Training"},{"subitem_title":"Adversarial Training: A Survey","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セッション4-A","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-03-03","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"中部大学"},{"subitem_text_value":"中部大学"},{"subitem_text_value":"中部大学"},{"subitem_text_value":"中部大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Chubu University","subitem_text_language":"en"},{"subitem_text_value":"Chubu University","subitem_text_language":"en"},{"subitem_text_value":"Chubu University","subitem_text_language":"en"},{"subitem_text_value":"Chubu University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/216954/files/IPSJ-CVIM22229023.pdf","label":"IPSJ-CVIM22229023.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM22229023.pdf","filesize":[{"value":"3.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"31d6ab13-94cc-4bd4-8423-f099d5742d72","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"足立, 浩規"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"平川, 翼"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山下, 隆義"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤吉, 弘亘"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hiroki, Adachi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tsubasa, Hirakawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takayoshi, Yamashita","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hironobu, Fujiyoshi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Adversarial training (AT) は悪意のある摂動を付与したサンプル (AEs: Adversarial examples) を学習に使用して，攻撃に頑健なモデルの獲得を目的とした学習方法である．AT は AEs に対するモデルの頑健性能を向上させる一方で，通常のサンプルに対する分類精度を大幅に劣化させる性質がある．この問題を解消するために，様々な観点か らアプローチした手法が数多く提案されている．本稿では AT についてサーベイし，AT の研究動向について体系的にまとめる．また，代表的な手法に関して，データセットやモデルなどを統一して分類精度の評価および比較をする． さらに，各手法を適用したモデルの低次元特徴空間を可視化しつつ，特徴空間の定量的評価指標を用いて比較をする．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Adversarial training (AT) is a training method that aims to obtain a robust model for defencing the adversarial attack by using adversarial examples (AEs). Although AT improves the robustness of the model to AEs, it significantly decreases the classification accuracy to natural samples. To overcome this problem, researchers proposed methods that approached from several perspectives. In this paper, we survey AT and systematically summarize about research trends of AT. Furthermore, we evaluate and compare the classification accuracy with the exact experimental details for the typical methods. Moreover, we visualize the low dimensional feature space of the model applied to each method and evaluate the feature representation using some quantitative evaluation indices.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"13","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-03-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"23","bibliographicVolumeNumber":"2022-CVIM-229"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216954,"updated":"2025-01-19T15:40:33.783688+00:00","links":{},"created":"2025-01-19T01:17:27.896418+00:00"}