{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214881","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"214881","title":["画像異常検知における事前学習モデルを用いた特徴抽出に関する考察"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"6514da6b-e676-45d6-b475-0f113fd93909"},"_deposit":{"id":"214881","pid":{"type":"depid","value":"214881","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"画像異常検知における事前学習モデルを用いた特徴抽出に関する考察","author_link":["553004","553003"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"画像異常検知における事前学習モデルを用いた特徴抽出に関する考察"}]},"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":"2021-03-04","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":"東大"},{"subitem_text_value":"東大"}]},"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/214881/files/IPSJ-Z83-1N-03.pdf","label":"IPSJ-Z83-1N-03.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-1N-03.pdf","filesize":[{"value":"194.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"2a39fe51-0c33-4c1e-aec9-746a43c6c5a4","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小野, 拓也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"森, 純一郎"}],"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":"画像異常検知はその実用性から注目を集め、近年は深層学習が高い性能を示している。一般に、異常サンプルの希少性と多様性からパターンの網羅に十分なサンプル数の確保は困難である。そのため、ラベルが不要な教師なし学習が活用されており、特に画像では次元削減のためにImageNet等で事前学習したCNNによる特徴抽出が有効である。しかし、転移学習を用いた手法は数多く存在する一方、データセットや抽出した特徴量の扱いなどに跨った比較は未だされていない。本論文では事前学習モデルと教師なし学習による異常検知を整理し、様々なデータで性能調査を行った。結果として得られた有効な手法例といくつかの重要な示唆について述べる。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"152","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"151","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214881,"updated":"2025-01-19T16:25:28.602456+00:00","links":{},"created":"2025-01-19T01:15:40.449580+00:00"}