{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00221013","sets":["6504:11035:11043"]},"path":["11043"],"owner":"44499","recid":"221013","title":["XAIを用いたノイズに頑健なモデル構築手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-17"},"_buckets":{"deposit":"e53143f3-80e4-4f40-974d-5257c9665503"},"_deposit":{"id":"221013","pid":{"type":"depid","value":"221013","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"XAIを用いたノイズに頑健なモデル構築手法の提案","author_link":["578412","578411","578410"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"XAIを用いたノイズに頑健なモデル構築手法の提案"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2022-02-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良高専"},{"subitem_text_value":"奈良高専"},{"subitem_text_value":"奈良高専"}]},"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/221013/files/IPSJ-Z84-6T-03.pdf","label":"IPSJ-Z84-6T-03.pdf"},"date":[{"dateType":"Available","dateValue":"2022-10-22"}],"format":"application/pdf","filename":"IPSJ-Z84-6T-03.pdf","filesize":[{"value":"372.8 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"46a626ee-1d7a-427a-9ef5-5f114de7f02a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"川端, 祐也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"市川, 嘉裕"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 智浩"}],"nameIdentifiers":[{}]}]},"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_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"敵対的サンプル（Adversarial Example）による攻撃に対しての最善策としてAdversarial Trainingが注目されている．しかし，この手法は通常の学習手法で作成されるモデルよりノイズに対しての認識精度が下がる傾向にあり，精度と頑健性はトレードオフの関係であることがわかっている．　本研究では，ノイズを含む対象に対する認識精度を上げる手法の提案を目的とする．そのために，学習済みのモデルに対してXAI手法の1つであるSHAPを適用して，モデルを欺くのに効果的なノイズを加えたデータを生成し，それを学習データとして再学習させる手法を提案する．敵対的サンプル等を含むノイズのある対象に対して，再学習したモデルの認識精度が向上するかどうかを検証する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"544","bibliographic_titles":[{"bibliographic_title":"第84回全国大会講演論文集"}],"bibliographicPageStart":"543","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:21:01.883503+00:00","updated":"2025-01-19T14:20:03.964453+00:00","id":221013}