{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00197662","sets":["1164:2735:9724:9827"]},"path":["9827"],"owner":"44499","recid":"197662","title":["深層教師なし異常部分検知のための偶然的不確実さを考盧した異常度"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-06-10"},"_buckets":{"deposit":"01cc762f-a3d7-4173-85e2-5c5d75b5283c"},"_deposit":{"id":"197662","pid":{"type":"depid","value":"197662","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深層教師なし異常部分検知のための偶然的不確実さを考盧した異常度","author_link":["474717","474722","474720","474721","474723","474716","474718","474719"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層教師なし異常部分検知のための偶然的不確実さを考盧した異常度"},{"subitem_title":"Aleatoric Uncertainty-Aware Score for Deep Unsupervised Anomaly Segmentation","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2019-06-10","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"神戸大学大学院システム情報学研究科計算科学専攻"},{"subitem_text_value":"神戸大学大学院システム情報学研究科計算科学専攻"},{"subitem_text_value":"神戸大学大学院システム情報学研究科計算科学専攻"},{"subitem_text_value":"神戸大学大学院システム情報学研究科計算科学専攻"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of System Infomatics, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of System Infomatics, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of System Infomatics, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of System Infomatics, Kobe 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/197662/files/IPSJ-MPS19123003.pdf","label":"IPSJ-MPS19123003.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS19123003.pdf","filesize":[{"value":"700.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"389a3b15-8fda-4c6b-99fb-767b92fb25dd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"佐藤, 一輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"濱, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松原, 崇"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"上原, 邦昭"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kazuki, Sato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenta, Hama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takashi, Matsubara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kuniaki, Uehara","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"画像をもとにした異常部分のセグメンテーションは，画像解析において基本的なトピックの一つである．特に，未知の系統の異常に対応するような場合には，教師なし学習による方法がよく用いられる．この場合，既知のサンプルの尤度を最大化するよう学習された確率モデルを用いて，推定された尤度が低いものを異常として検出する．\nしかし，これらのモデルは意味論的な異常よりもデータが持つ複雑な構造に敏感になる傾向がある．本論文では，対数尤度の近似からデータの複雑性を反映する項を取り除くことで，異常セグメンテーションにおける不確実性を考慮した新しい異常度を提案する．実験結果により，提案した異常度がデータの複雑性に対して頑健であることを示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Image-based anomaly segmentation is a fundamental topic in the field of image analysis. Especially, an unsupervised approach is preferred when dealing with unknown types of anomalies. For this purpose, probabilistic models trained to maximize the likelihood of known samples are often employed. They identify samples with low estimated likelihoods as anomalies. However, they have a tendency to react to complex structures rather than semantic anomalies. This paper proposes a novel uncertainty-aware score for anomaly segmentation by removing the term that reflects the data complexity from the approximated log-likelihood. Experimental results demonstrate that the proposed score is robust against data complexity.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-06-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2019-MPS-123"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":197662,"updated":"2025-01-19T22:15:45.371869+00:00","links":{},"created":"2025-01-19T01:02:05.836337+00:00"}