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pro vyhledávání: '"Maeda, Shunta"'
Autor:
Maeda, Shunta
We propose a generative model termed Deciphering Autoencoders. In this model, we assign a unique random dropout pattern to each data point in the training dataset and then train an autoencoder to reconstruct the corresponding data point using this pa
Externí odkaz:
http://arxiv.org/abs/2310.01712
Autor:
Maeda, Shunta
Since the first success of Dong et al., the deep-learning-based approach has become dominant in the field of single-image super-resolution. This replaces all the handcrafted image processing steps of traditional sparse-coding-based methods with a dee
Externí odkaz:
http://arxiv.org/abs/2207.09228
Publikováno v:
In Journal of Psychiatric Research November 2024 179:8-14
Autor:
Maeda, Shunta
In most studies on learning-based image super-resolution (SR), the paired training dataset is created by downscaling high-resolution (HR) images with a predetermined operation (e.g., bicubic). However, these methods fail to super-resolve real-world l
Externí odkaz:
http://arxiv.org/abs/2002.11397
Autor:
Maeda, Shunta
Fast and flexible processing are two essential requirements for a number of practical applications of image denoising. Current state-of-the-art methods, however, still require either high computational cost or limited scopes of the target. We introdu
Externí odkaz:
http://arxiv.org/abs/1911.08724
Autor:
Takahashi, Kento, Fujikawa, Mayu, Ueno, Takashi, Ogawa, Maimi, Nakasato, Nobukazu, Maeda, Shunta
Publikováno v:
In Epilepsy & Behavior December 2023 149
Autor:
Maeda, Shunta
Publikováno v:
In Psychoneuroendocrinology October 2022 144
Publikováno v:
In Comprehensive Psychoneuroendocrinology August 2022 11
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Publikováno v:
In Comprehensive Psychoneuroendocrinology August 2021 7