{"created":"2023-07-27T07:53:34.804235+00:00","id":29397,"links":{},"metadata":{"_buckets":{"deposit":"244be3da-7e64-4f1e-87e3-edf99cb4b92b"},"_deposit":{"created_by":21,"id":"29397","owners":[21],"pid":{"revision_id":0,"type":"depid","value":"29397"},"status":"published"},"_oai":{"id":"oai:doshisha.repo.nii.ac.jp:00029397","sets":["4251:8138:8139:8140:9229","8:3372:3847:9228"]},"author_link":["30839","30491","18373","26005"],"item_1693811493084":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1694490770713":{"attribute_name":"権利者情報","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"DA18202107","nameIdentifierScheme":"AID"}],"rightHolderNames":[{"rightHolderLanguage":"ja","rightHolderName":"同志社大学ハリス理化学研究所"},{"rightHolderLanguage":"en","rightHolderName":"Harris Science Research Institute of Doshisha University"}]}]},"item_1_biblio_info_14":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2023-01-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicPageEnd":"187","bibliographicPageStart":"181","bibliographicVolumeNumber":"63","bibliographic_titles":[{"bibliographic_title":"同志社大学ハリス理化学研究報告","bibliographic_titleLang":"ja"},{"bibliographic_title":"The Harris science review of Doshisha University","bibliographic_titleLang":"en"}]}]},"item_1_description_12":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"様々なNeural Networkモデルが提案されているが，ビッグデータの出現によりモデルの学習や利用には高い計算能力が要求される．十分な計算機が利用できない場合は，小規模で高性能なモデルを構築することは困難である．そこで，我々は差分進化に基づくモデル圧縮手法を提案する．実験の結果，提案手法は重みパラメータを適切に削減し，圧縮率を調整することで圧縮しない元のモデルと同程度の精度を持つことが確認された．","subitem_description_language":"ja","subitem_description_type":"Abstract"},{"subitem_description":"Various Deep Neural Network models have been proposed; however, with the emergence of Big Data, high  computing power is required to train and use such models. A small adequate model should be modeled if a rich computing environment is unavailable, but it is challenging to clarify how to build a relatively small, highperformance model. Therefore, we propose a model compression method based on differential evolution. Specifically, the proposed method optimizes not only network structures and also weights simultaneously by differential evolution. Experiment results showed that the proposed method appropriately reduced weight parameters during optimization and had similar accuracy as the original model without compression by adjusting the compression rate.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_1_description_25":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_1_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.14988/00029396","subitem_identifier_reg_type":"JaLC"}]},"item_1_publisher_15":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"同志社大学ハリス理化学研究所","subitem_publisher_language":"ja"}]},"item_1_publisher_16":{"attribute_name":"出版者（英）","attribute_value_mlt":[{"subitem_publisher":"Harris Science Research Institute of Doshisha 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/ 同志社大学大学院理工学研究科"},{"subitem_text_language":"ja","subitem_text_value":"幾島, 直哉 / 同志社大学大学院理工学研究科"},{"subitem_text_language":"ja","subitem_text_value":"小野, 景子 / 同志社大学理工学部インテリジェント情報工学科准教授"},{"subitem_text_language":"ja","subitem_text_value":"槇原, 絵里奈 / 同志社大学理工学部インテリジェント情報工学科助教"}]},"item_1_text_9":{"attribute_name":"著者所属（英）","attribute_value_mlt":[{"subitem_text_language":"en","subitem_text_value":"Hanamoto, Nagi / Graduate School of Science and Engineering, Doshisha University"},{"subitem_text_language":"en","subitem_text_value":"Ikushima, Naoya / Graduate School of Science and Engineering, Doshisha University"},{"subitem_text_language":"en","subitem_text_value":"Ono, Keiko / Faculty of Science and Engineering, Doshisha University"},{"subitem_text_language":"en","subitem_text_value":"Makihara,  Erina / Faculty of Science and Engineering, Doshisha University"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open 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Erina","creatorNameLang":"en"}],"nameIdentifiers":[{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2023-02-13"}],"displaytype":"detail","filename":"023063040002.pdf","filesize":[{"value":"993.4 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"023063040002.pdf","url":"https://doshisha.repo.nii.ac.jp/record/29397/files/023063040002.pdf"},"version_id":"73a55eef-2d1c-4fab-8714-91ce0fdabaff"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"差分進化","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"モデル圧縮","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"枝切り","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"differential evolution","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"model compression","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Pruning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"MNIST","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"適応的差分進化を用いたNeural Networkにおけるモデル圧縮","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"適応的差分進化を用いたNeural Networkにおけるモデル圧縮","subitem_title_language":"ja"},{"subitem_title":"テキオウテキ サブン シンカ オ モチイタ ニューラル ネットワーク ニオケル モデル アッシュク","subitem_title_language":"ja-Kana"},{"subitem_title":"Model compression optimization in neural network using adaptive differential evolution","subitem_title_language":"en"}]},"item_type_id":"1","owner":"21","path":["9228","9229"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-02-13"},"publish_date":"2023-02-13","publish_status":"0","recid":"29397","relation_version_is_last":true,"title":["適応的差分進化を用いたNeural Networkにおけるモデル圧縮"],"weko_creator_id":"21","weko_shared_id":-1},"updated":"2024-01-26T05:51:01.331090+00:00"}