{"created":"2025-01-19T01:37:50.568496+00:00","updated":"2025-01-19T09:25:38.581283+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236018","sets":["6504:11678:11689"]},"path":["11689"],"owner":"44499","recid":"236018","title":["GAN追加学習のためのFréchet Inception Distanceに基づくデータセットの選別手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"2f62d531-2704-4224-8ec4-080e184bf44b"},"_deposit":{"id":"236018","pid":{"type":"depid","value":"236018","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"GAN追加学習のためのFréchet Inception Distanceに基づくデータセットの選別手法の提案","author_link":["645001","645000"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"GAN追加学習のためのFréchet Inception Distanceに基づくデータセットの選別手法の提案"}]},"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":"2024-03-01","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":"明星大"},{"subitem_text_value":"明星大"}]},"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/236018/files/IPSJ-Z86-7Q-06.pdf","label":"IPSJ-Z86-7Q-06.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-03"}],"format":"application/pdf","filename":"IPSJ-Z86-7Q-06.pdf","filesize":[{"value":"315.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"63e4977d-46b4-4feb-92b1-34d9030d4555","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"西澤, 桂祐"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"和田, 康孝"}],"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":"GAN（Generative Adversarial Network）は生成AIのための有力な手法として広く研究開発されているが，多数のデータを用いた学習プロセスを多数繰り返す必要がある． 本稿では，GANの性能を比較するための指標としてよく用いられるFID（Fréchet Inception Distance）を活用し，GANの学習に効果のあるデータを選別することで，学習の効率を向上させる手法を提案する． 画像群の間の類似性を測るFIDに基づいて画像を選別することにより，どの程度，生成画像の品質を保ちつつ学習に要する時間を短縮できるかを評価する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"350","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"349","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":236018,"links":{}}