{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00182730","sets":["934:989:9056:9194"]},"path":["9194"],"owner":"11","recid":"182730","title":["畳み込みニューラルネットワークを用いた画像分類タスクの直感的可視化方法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-07-19"},"_buckets":{"deposit":"73bf63d2-9d6a-468c-8136-28fb155cf176"},"_deposit":{"id":"182730","pid":{"type":"depid","value":"182730","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"畳み込みニューラルネットワークを用いた画像分類タスクの直感的可視化方法","author_link":["398800","398803","398802","398801"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"畳み込みニューラルネットワークを用いた画像分類タスクの直感的可視化方法"},{"subitem_title":"Intuitive Visualization Method for Image Classification Using Convolutional Neural Networks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] 深層学習，畳み込みニューラルネットワーク，画像分類，可視化","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2017-07-19","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"横浜国立大学大学院環境情報学府"},{"subitem_text_value":"横浜国立大学大学院環境情報学府"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Environment and Information Sciences, Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Environment and Information Sciences, Yokohama National 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/182730/files/IPSJ-TOM1002002.pdf","label":"IPSJ-TOM1002002.pdf"},"date":[{"dateType":"Available","dateValue":"2019-07-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1002002.pdf","filesize":[{"value":"1.9 MB"}],"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":"f5a5d71a-baea-4d8b-9a67-04fe81546748","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"荒井, 敏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"長尾, 智晴"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Satoshi, Arai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoharu, Nagao","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"深層ニューラルネットワークは画像認識の様々な分野で目覚ましい成果をあげているが，今後の産業応用を考えるうえでは解決すべき課題も残されている．一例として，画像分類のタスクでは分類結果をラベルとして出力するだけではなく，画像中のどの部位に着目して分類がなされたか，分類の根拠を示すよう求められる場合がある．筆者らはこの問題を解決するシンプルな構成のネットワークを提案する．提案手法では分類スコアと直接対応する可視化用のマップが分類タスクの過程で生成され，視覚的に確認可能なマップが分類結果に自然な形で反映される．ベンチマーク用画像を用いて実験を行い，本手法が可視化手法として有効であることを示す．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Deep neural networks show excellent performance in various image recognition field. However, some issues remain for future industrial applications. For example, in image classification tasks, users might request not only to estimate class label but also to answer where the system give attention to classify. We propose novel network architecture to solve this issue. Our method generates 2D maps directly related to classification scores during classification, and generated maps are visually recognizable and reflected to classification result naturally. We empirically indicate effect of our method for existing datasets.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"13","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2017-07-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"10"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"updated":"2025-01-20T03:58:06.094618+00:00","created":"2025-01-19T00:50:18.821077+00:00","links":{},"id":182730}