Skip to content

leon-bungert/Eigenvectors-of-Proximal-Operators-and-Neural-Networks

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 

Repository files navigation

Eigenvectors of Proximal Operators and Neural Networks

This Python and MATLAB code allows to reproduce some of the results of Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks [1]. Feel free to use it and please refer to our paper when doing so.

Prerequistes

The code for the proximal power method is written in Python and requires the Operator Discretization Library (ODL) https://odlgroup.github.io/odl/index.html. The code for the power method for the denoising neural network FFDnet [2] is written in MATLAB.

References

[1] Leon Bungert, Ester Hait-Fraenkel, Nicolas Papadakis, and Guy Gilboa. "Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks." arXiv preprint arXiv:2003.04595 (2020). https://arxiv.org/abs/2003.04595

[2] Kai Zhang, Wangmeng Zuo, and Lei Zhang. "FFDNet: Toward a fast and flexible solution for CNN-based image denoising." IEEE Transactions on Image Processing 27, no. 9 (2018): 4608-4622.

About

Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published
pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy