Feb 7, 2020 · To address this problem, we consider the simple prototypical problem of signal denoising, where classical approaches such as nonlinear diffusion ...

Jun 7, 2020 · Our results question two architectural principles behind CNNs that are usually taken for granted.

This work considers the simple prototypical problem of signal denoising, where classical approaches such as nonlinear diffusion, wavelet-based methods and ...

To address this problem, we consider the simple prototypical problem of signal denoising, where classical approaches such as nonlinear diffusion, wavelet-based ...

Translating diffusion, wavelets, and regularisation into residual networks. T Alt, J Weickert, P Peter. arXiv preprint arXiv:2002.02753, 2020. 12, 2020.

Translating Diffusion, Wavelets, and Regularisation into Residual Networks ... We connect these concepts to residual networks, recurrent neural networks, and U- ...

Weickert, P. Peter: Translating Diffusion, Wavelets, and Regularisation into Residual Networks. arXiv:2002.02753 [cs.LG], February 2020.

Diffusion block for one explicit nonlinear diffusion step (6).. Translating Diffusion, Wavelets, and Regularisation into Residual Networks. Preprint. Full ...

Nov 21, 2024 · Nonlinear diffusion filtering and wavelet shrinkage are two methods that serve the same purpose, namely discontinuity-preserving denoising.

Apr 30, 2021 · We investigate what can be learned from translating numerical algorithms into neural networks. On the numerical side, we consider explicit, ...