{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232731","sets":["1164:4619:11539:11552"]},"path":["11552"],"owner":"44499","recid":"232731","title":["メタ学習におけるタスク密度比の推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-02-25"},"_buckets":{"deposit":"004c5bdd-a1e9-4031-b394-d0552a219d33"},"_deposit":{"id":"232731","pid":{"type":"depid","value":"232731","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"メタ学習におけるタスク密度比の推定","author_link":["630726","630729","630730","630728","630727","630731"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"メタ学習におけるタスク密度比の推定"},{"subitem_title":"Lei Sun;;Yusuke Tanaka;;Tomoharu Iwata","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-02-25","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良先端科学技術大学院大学"},{"subitem_text_value":"NTT"},{"subitem_text_value":"NTT"}]},"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/232731/files/IPSJ-CVIM24237040.pdf","label":"IPSJ-CVIM24237040.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM24237040.pdf","filesize":[{"value":"1.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"f9664e02-53d7-4ce3-a0ac-40e7814d71d1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 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":"孫, 雷"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 佑典"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"岩田, 具治"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Lei, Sun","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoharu, Iwata","creatorNameLang":"en"}],"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":"メタ学習では，通常，メタ学習時およびメタテスト時に用いられるタスクは同一のタスク分布から抽出されると仮定する．しかし実世界応用において，この仮定が成り立たないことも多々あり，その際，既存のメタ学習法は良い性能が得られない．本研究では，このようなタスクシフトの状況下において性能を向上させるメタ学習法「重要度重み付きメタ学習」を提案する．本手法では，メタテストタスクの分布から得られるタスクのラベルなしデータを活用し，各メタ学習タスクに対して重みを割り当て，メタテストタスクと近いメタ学習タスクに重みをおいてメタ学習する．重みは，メタテストとメタ学習タスクの確率密度の比に基づいて算出する．この重みを用いたメタ学習タスクの期待誤差は，メタテストタスク上の期待誤差に対する不偏推定量となる．タスク間の距離に Maximum Mean Discrepancy を用いたカーネル密度推定を用いて，タスク密度を推定する．Few-shot 分類データセットを用いた実験により，本手法が既存のメタ学習手法およびタスク拡張手法に比べて顕著な性能向上を達成することを示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","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":"2024-02-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"40","bibliographicVolumeNumber":"2024-CVIM-237"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":232731,"updated":"2025-01-19T10:20:23.596558+00:00","links":{},"created":"2025-01-19T01:33:45.856952+00:00"}