{"id":209808,"created":"2025-01-19T01:11:05.648088+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00209808","sets":["1164:4619:10416:10532"]},"path":["10532"],"owner":"44499","recid":"209808","title":["階層化Neural Processを用いた高解像度の部分画像補完"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-02-25"},"_buckets":{"deposit":"7bb1253e-5a24-4aba-81cb-e1f952060f7f"},"_deposit":{"id":"209808","pid":{"type":"depid","value":"209808","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"階層化Neural Processを用いた高解像度の部分画像補完","author_link":["529928","529925","529931","529927","529923","529929","529924","529922","529930","529926"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"階層化Neural Processを用いた高解像度の部分画像補完"},{"subitem_title":"High-Resolution Image Completion by Hierarchical Neural Process","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セッション1-2","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-02-25","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"NTTサービスエボリューション研究所"},{"subitem_text_value":"NTTサービスエボリューション研究所"},{"subitem_text_value":"NTTサービスエボリューション研究所"},{"subitem_text_value":"NTTサービスエボリューション研究所"},{"subitem_text_value":"NTTサービスエボリューション研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"NTT Service Evolution Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Service Evolution Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Service Evolution Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Service Evolution Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Service Evolution Laboratories","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/209808/files/IPSJ-CVIM21225010.pdf","label":"IPSJ-CVIM21225010.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM21225010.pdf","filesize":[{"value":"1.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"3a7025e7-affa-452b-8c69-79aacce81dcd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 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":"宮原, 雅人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐藤, 大佑"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"福田, 匡人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松村, 成宗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西川, 嘉樹"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masato, Miyahara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Sato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Fukuda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Narimune, Matsumura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshiki, Nishikawa","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":"Neural Process(NP) は，予測の不確実性を考慮可能な深層生成モデルである．形状未知の関数に従う既知の入出力データを学習データとし，元の関数の確率的な表現を潜在変数として学習することで未知の出力の推定を行う．一方，NP は潜在変数が従う分布としてガウス分布を仮定するため，潜在変数が非ガウスの複雑な分布で表現される関数に従うデータに適応するのが困難である．本研究では，NP の潜在変数を階層化した階層化 NP を提案する．NP の階層化に加え，上位層の潜在変数の学習を効率化すべく，スキップパスの導入と双方向の潜在変数の導入した． 一次関数の合成関数を用いた比較実験の結果，シンプルな条件設定では提案手法の予測性能は既存手法よりも劣ることが示された．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Neural Process (NP) is a deep generation model which can consider the uncertainty of prediction. The unknown output is estimated by learning the stochastic expression of the original function as a latent variable using the known input/output data following the shape unknown function as learning data. On the other hand, NP assumes Gaussian distribution as a distribution followed by latent variables, so it is difficult to adapt to data following a function represented by a complex distribution with non-Gaussian latent variables. In this paper, we propose a hierarchical NP which is a hierarchical representation of the latent variables of NPs. In addition to the hierarchy of NPs, we introduce skip paths and bidirectional latent variables in order to improve the learning efficiency of latent variables in upper layers. As a result of the comparison experiment using the composite function of the linear function, it was shown that the prediction performance of the proposed method was inferior to the existing method in the simple condition setting.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","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":"2021-02-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2021-CVIM-225"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:23:00.877586+00:00","links":{}}