{"created":"2025-01-19T01:28:22.569375+00:00","updated":"2025-01-19T11:35:25.743246+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229242","sets":["1164:4619:11188:11420"]},"path":["11420"],"owner":"44499","recid":"229242","title":["Web上の類似画像検索と多様性クエリ戦略の組み合わせによる表現学習の性能向上"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-11-09"},"_buckets":{"deposit":"888f0b8e-aec2-4b69-bec6-dc0151a3f62f"},"_deposit":{"id":"229242","pid":{"type":"depid","value":"229242","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Web上の類似画像検索と多様性クエリ戦略の組み合わせによる表現学習の性能向上","author_link":["616122","616121"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Web上の類似画像検索と多様性クエリ戦略の組み合わせによる表現学習の性能向上"}]},"item_type_id":"4","publish_date":"2023-11-09","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岐阜大学工学部"},{"subitem_text_value":"岐阜大学工学部"}]},"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/229242/files/IPSJ-CVIM23235022.pdf","label":"IPSJ-CVIM23235022.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM23235022.pdf","filesize":[{"value":"1.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"f20c5235-2fa1-4d72-8820-7228c6f9a4d2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 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":"上野, 詩翔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"加藤, 邦人"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","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":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"大規模な学習データを要する深層学習において，アノテーションは労力を要する作業である．そこで，表現学習の 1 つである自己教師あり学習 (SSL: Self-Supervised Learning) では，機械的に生成された疑似ラベルを用いて学習を行う．SSL は人によるアノテーション作業が不要なため，データセットの構築に掛かるコストが低く，教師あり学習と比べて大規模なデータセットで学習させることが容易である．しかし，撮影条件などにより大量の画像を収集することが困難な場合は，データセットが小規模となり，SSL の性能が低下する．そこで本研究では，類似画像検索と能動学習の多様性クエリ戦略を用いて，Web 上から収集した画像を選択的に訓練データに追加して学習する手法を提案する．提案手法は類似画像検索により，下流タスクに関連性の高い画像を Web 上から収集しつつ，多様性クエリ戦略を用いてデータセットの多様性を維持する画像を訓練データに追加することが可能である．実験により，提案手法が下流タスクの訓練データが少数の場合において SSL の性能を向上させることを示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-11-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"22","bibliographicVolumeNumber":"2023-CVIM-235"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":229242,"links":{}}