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Efficient decomposition of latent representation in generative models
https://oist.repo.nii.ac.jp/records/2002
https://oist.repo.nii.ac.jp/records/200276d134dd-289e-4965-beb2-48f6dccf2d79
名前 / ファイル | ライセンス | アクション |
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IEEE_conference_accepted_version (1.4 MB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2021-03-30 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Efficient decomposition of latent representation in generative models | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Nikulin, Vsevolod
× Nikulin, Vsevolod× Tani, Jun |
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書誌情報 |
en : 2020 IEEE Symposium Series on Computational Intelligence (SSCI) p. 611-615, 発行日 2021-01-05 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In designing self-organizing generative models of robot behaviour, it is important to address the issue of generalization for multiple patterns, while keeping latent representation in a low dimension. We are investigating the possibility of introducing local coordinates for samples of each pattern in shared latent representation. Moreover, recent advances in efficient, approximate computation of Jacobians allows us to introduce specific regularization that ensures directional robustness in introduced local coordinates. | |||||
出版者 | ||||||
出版者 | IEEE | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1109/SSCI47803.2020.9308173 | |||||
権利 | ||||||
権利情報 | ©2020 IEEE | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://ieeexplore.ieee.org/document/9308173 | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa |