{"created":"2025-01-19T01:29:19.339243+00:00","updated":"2025-01-19T11:22:33.048218+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229889","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"229889","title":["学習済みパラメータの分析に基づくモデル圧縮と転移学習への応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"cb6e6c86-5ce4-453c-bc44-50ca3736bea0"},"_deposit":{"id":"229889","pid":{"type":"depid","value":"229889","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"学習済みパラメータの分析に基づくモデル圧縮と転移学習への応用","author_link":["618416","618415"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"学習済みパラメータの分析に基づくモデル圧縮と転移学習への応用"}]},"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":"法大"},{"subitem_text_value":"法大"}]},"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/229889/files/IPSJ-Z85-7P-08.pdf","label":"IPSJ-Z85-7P-08.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-7P-08.pdf","filesize":[{"value":"517.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"99aec9be-8dbb-4b3a-90c0-49cca379bb44","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":"廣瀬, 理陽"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤田, 悟"}],"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":"深層学習では一般に，学習パラメータが膨大である巨大なモデルほど高い性能を示すが，大きなメモリサイズを必要とする難点がある．これを解決するため提案された知識蒸留は，巨大モデルの出力を小さいモデルに学習させることで，モデルの圧縮を実現した．しかしこの手法では，巨大モデルが学習したパラメータを継承することはなく，初期化されたパラメータからの学習により，多くの学習時間を要する．本論文では，巨大モデルが学習したパラメータはパラメータ空間内に偏在することに注目し，それらが形成するクラスタに基づいてパラメータを集約することで，モデルを圧縮する手法を提案する．また，本手法による転移学習への有用性も検証する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"180","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"179","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":229889,"links":{}}