{"updated":"2025-01-19T11:17:15.612049+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230107","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230107","title":["Group DROによる行動分節化モデルの堅牢性能改善の実証的評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"22b3891a-ab47-4d68-919d-cbc342f32b91"},"_deposit":{"id":"230107","pid":{"type":"depid","value":"230107","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Group DROによる行動分節化モデルの堅牢性能改善の実証的評価","author_link":["619065","619064"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Group DROによる行動分節化モデルの堅牢性能改善の実証的評価"}]},"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":"2023-02-16","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":"静岡大"},{"subitem_text_value":"静岡大"}]},"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/230107/files/IPSJ-Z85-5U-04.pdf","label":"IPSJ-Z85-5U-04.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-5U-04.pdf","filesize":[{"value":"609.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"8ea83b50-935d-49d0-b8f2-4f219f648538","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 泰生"}],"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":"一般の機械学習モデルはi.i.d.データで高精度となるように, 学習時に経験損失最小化の戦略を取る. しかし, 現実の問題では, 分布シフトなどが起因してこの前提は破綻する. そこで, シフトに頑健な学習法を導入する事により, 多数派と分布が異なるデータに対して, 汎化誤差が小さくなる様に学習を進める事が期待できる. 本研究では, 部品組立作業の行動分節化の問題を対象とし, 分布シフト問題におけるGroup DROの可用性を検証する. 様々な要因によりデータに分布のズレが生じ, ベースラインモデルではデータにより精度が低い場合がある. 本稿では, Group DROにより行動分節化モデルの汎化性能がどれ程改善するかを調査した.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"634","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"633","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230107,"created":"2025-01-19T01:29:40.214674+00:00"}