{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216564","sets":["1164:2735:10865:10866"]},"path":["10866"],"owner":"44499","recid":"216564","title":["クラスに依存しない潜在表現の獲得による継続学習における安定性と可塑性のジレンマの解消"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-24"},"_buckets":{"deposit":"884b9e0c-2e53-4f54-ad47-5e518c258598"},"_deposit":{"id":"216564","pid":{"type":"depid","value":"216564","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"クラスに依存しない潜在表現の獲得による継続学習における安定性と可塑性のジレンマの解消","author_link":["559083","559082","559084"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"クラスに依存しない潜在表現の獲得による継続学習における安定性と可塑性のジレンマの解消"},{"subitem_title":"Breaking Down Stability-Plasticity Dilemma in Continual Learning by Acquiring Class-Agnostic Features","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-02-24","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"青山学院大学大学院理工学研究科"},{"subitem_text_value":"青山学院大学理工学部"},{"subitem_text_value":"青山学院大学理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Engineering, Aoyama Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"College of Science and Engineering, Aoyama Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"College of Science and Engineering, Aoyama Gakuin 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/216564/files/IPSJ-MPS22137018.pdf","label":"IPSJ-MPS22137018.pdf"},"date":[{"dateType":"Available","dateValue":"2024-02-24"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS22137018.pdf","filesize":[{"value":"465.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"d0684e2b-c417-4cb3-90b6-94b8de3efa5c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"村田, 健悟"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"伊東, 聖矢"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大原, 剛三"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","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":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，深層学習モデルを対象とし，Class Incremental Learning（CIL）と呼ばれる，分類対象クラスが増加する継続学習のシナリオについて広く研究されている．CIL では，モデルが既学習クラスへの識別性能を保持可能なほどに安定，かつ，新規クラスについて十分学習できるほどに可塑である必要がある．この相反する要件は，安定性と可塑性のジレンマと呼ばれ，このジレンマの解消が CIL における課題の一つである．そこで本研究では，新規クラスの識別にも有用な潜在表現を事前に獲得することが学習に必要な可塑性を軽減し，このジレンマの解消につながり得ることに着目し，クラスに依存しない潜在表現の獲得を促す学習フレームワークを提案する．提案フレームワークでは，その表現を分類に適した表現へ変換するメカニズムを導入し，各クラスに対する分類精度の低下を抑制する．ベンチマークデータを利用した実験では，本フレームワークを既存手法に適用することで，最終的な全クラスに対する平均精度が向上することを示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-24","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"18","bibliographicVolumeNumber":"2022-MPS-137"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216564,"updated":"2025-01-19T15:48:14.559402+00:00","links":{},"created":"2025-01-19T01:17:06.455355+00:00"}