Deep Networks from the Principle of Rate Reduction
redunet_paper
Deep Networks from the Principle of Rate Reduction
This repository is the official NumPy implementation of the paper Deep Networks from the Principle of Rate Reduction (2021) by Kwan Ho Ryan Chan* (UC Berkeley), Yaodong Yu* (UC Berkeley), Chong You* (UC Berkeley), Haozhi Qi (UC Berkeley), John Wright (Columbia), and Yi Ma (UC Berkeley). For PyTorch version of ReduNet, please visit https://github.com/ryanchankh/redunet.
What is ReduNet?
ReduNet is a deep neural network construcuted naturally by deriving the gradients of the Maximal Coding Rate Reduction (MCR2) [1] objective. Every layer of this network can be interpreted based on its mathematical operations and the network collectively is trained in a feed-forward manner only. In addition, by imposing shift invariant properties to our network, the convolutional