{"created":"2023-07-27T07:52:46.377448+00:00","id":28392,"links":{},"metadata":{"_buckets":{"deposit":"db6b6c55-e674-46ab-9885-7d057962ebc6"},"_deposit":{"created_by":21,"id":"28392","owners":[21],"pid":{"revision_id":0,"type":"depid","value":"28392"},"status":"published"},"_oai":{"id":"oai:doshisha.repo.nii.ac.jp:00028392","sets":["4251:8138:8139:8140:8910","8:3372:3847:8909"]},"author_link":["30408","22123"],"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","nameIdentifierURI":"https://ci.nii.ac.jp/author/DA18202107"}],"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":"2021-07-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicPageEnd":"116","bibliographicPageStart":"111","bibliographicVolumeNumber":"62","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":"畳み込みニューラルネットワーク(Convolutional neural network, CNN)モデルは、既存の方法と比べ分類性能が優れており、画像分類における深層学習のベースになっている。CNNモデルの正解率(Accuracy)を上げるためにアンサンブル学習方法が提案されているが、アンサンブル学習方法は複数のモデルを用いるため、訓練時間と予測時間が長くなる。本論文では、分裂畳み込みニューラルネットワークというアンサンブル学習モデルを提案する。分裂畳み込みニューラルネットワークはオリジナル方法と比べて、同程度の正解率を確保しつつ、訓練時間と予測時間を減少させることができる。減少できた時間は選択したバックボーンによって異なり、最も多かった場合、約50%の時間を減少することができた。","subitem_description_language":"ja","subitem_description_type":"Abstract"},{"subitem_description":"Convolutional neural network (CNN) models have become the basis for deep learning in image classification because of their superior performance. Ensemble learning is effective in increasing the accuracy of CNN models. However, ensemble learning requires greater training and implementation time than a CNN model. In this paper, we present a split convolutional ensemble model that costs less in terms of training and implementation time, with same accuracy as that of the original ensemble method. This method can save up to 50% of time compared with the original ensemble method.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_1_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.14988/00028384","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 University","subitem_publisher_language":"en"}]},"item_1_relation_24":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"掲載刊行物所蔵情報へのリンク ／ Link to Contents"}],"subitem_relation_type":"isFormatOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doors.doshisha.ac.jp/opac/opac_link/bibid/SB12902196/?lang=0","subitem_relation_type_select":"URI"}}]},"item_1_source_id_17":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"21895937","subitem_source_identifier_type":"PISSN"}]},"item_1_source_id_19":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12716107","subitem_source_identifier_type":"NCID"}]},"item_1_subject_27":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"007.13","subitem_subject_scheme":"NDC"}]},"item_1_text_8":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_language":"ja","subitem_text_value":"朴, 健 / 同志社大学文化情報学研究科文化情報学専攻博士課程前期課程終了"},{"subitem_text_language":"ja","subitem_text_value":"金, 明哲 / 同志社大学文化情報学研究科教授"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"朴, 健","creatorNameLang":"ja"},{"creatorName":"ボク, ケン","creatorNameLang":"ja-Kana"},{"creatorName":"Jian, Piao","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"30408","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"9000405637507","nameIdentifierScheme":"CiNii ID","nameIdentifierURI":"http://ci.nii.ac.jp/nrid/9000405637507"}]},{"creatorNames":[{"creatorName":"Jin, Mingzhe","creatorNameLang":"en"},{"creatorName":"金, 明哲","creatorNameLang":"ja"},{"creatorName":"キン, メイテツ","creatorNameLang":"ja-Kana"}],"familyNames":[{"familyName":"Jin","familyNameLang":"en"},{"familyName":"金","familyNameLang":"ja"},{"familyName":"キン","familyNameLang":"ja-Kana"}],"givenNames":[{"givenName":"Mingzhe","givenNameLang":"en"},{"givenName":"明哲","givenNameLang":"ja"},{"givenName":"メイテツ","givenNameLang":"ja-Kana"}],"nameIdentifiers":[{"nameIdentifier":"22123","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"1000060275469","nameIdentifierScheme":"CiNii ID","nameIdentifierURI":"http://ci.nii.ac.jp/nrid/1000060275469"},{"nameIdentifier":"60275469","nameIdentifierScheme":"e-Rad_Researcher","nameIdentifierURI":"https://kaken.nii.ac.jp/ja/search/?qm=60275469"},{"nameIdentifier":"DA11224655","nameIdentifierScheme":"AID","nameIdentifierURI":"https://ci.nii.ac.jp/author/DA11224655"},{"nameIdentifier":"0000-0002-6012-1379","nameIdentifierScheme":"ORCID","nameIdentifierURI":"https://orcid.org/0000-0002-6012-1379"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-08-23"}],"displaytype":"detail","fileDate":[{"fileDateType":"Issued","fileDateValue":"2021-07-31"}],"filename":"023062020006.pdf","filesize":[{"value":"590.3 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"023062020006.pdf","url":"https://doshisha.repo.nii.ac.jp/record/28392/files/023062020006.pdf"},"version_id":"6abe53cd-5e97-4c1d-8355-9aae8ed2564b"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"畳み込みニューラルネットワーク","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"画像分類","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"集団学習","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"訓練時間","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"予測時間","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"CNN","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"image classification","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"ensemble learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"training","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"implementation time","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":"画像分類におけるアンサンブル深層学習の加速化モデルの提案","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"画像分類におけるアンサンブル深層学習の加速化モデルの提案","subitem_title_language":"ja"},{"subitem_title":"ガゾウ ブンルイ ニオケル アンサンブル シンソウ ガクシュウ ノ カソクカ モデル ノ テイアン","subitem_title_language":"ja-Kana"},{"subitem_title":"A proposal for an accelerated model of ensemble deep learning in image classification","subitem_title_language":"en"}]},"item_type_id":"1","owner":"21","path":["8909","8910"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-08-23"},"publish_date":"2021-08-23","publish_status":"0","recid":"28392","relation_version_is_last":true,"title":["画像分類におけるアンサンブル深層学習の加速化モデルの提案"],"weko_creator_id":"21","weko_shared_id":-1},"updated":"2025-10-23T04:21:49.445210+00:00"}