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For deep neural network models, we focus on its application to image denoising. We first demonstrate how an optimal procedure leveraging deep neural networks ...
[PDF] Computational Methods for Matrix/Tensor Factorization and ...
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In this thesis, we are interested in the computational aspects of feature learning. We focus on rank matrix and tensor factorization and deep neural network ...
Jun 10, 2021 · Chiang and Sullivan [31] were the first to use CNN (deep learning) for image denoising tasks. A neural network (weighting factor) was used to ...
Our strategy is different from not only the traditional EM algorithm for solving matrix/tensor de- composition models, but also conventional alternative least.
Sep 6, 2024 · This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning ...
Jul 10, 2024 · The three groups are biomedical image completion, biomedical image denoising, and information fusion. Second, the algorithms have been ...
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Feb 9, 2023 · To solve these issues, we combine both the deep denoising priors with low-rank tensor factorization. (DP-LRTF) for HSI restoration. The proposed ...
Abstract:The denoising of magnetic resonance (MR) images is important to improve the accuracy of organ tissue information recognition in the process of ...
Nov 9, 2024 · DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks.
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We provide an overview of matrix and tensor factorization methods from a Bayesian perspective, giving emphasis on both the inference methods and modeling ...
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