This repository provides the implementation of Deep Variational Information Bottleneck. The main idea of DVIB is to impose a bottleneck (here in the dimensionality) through which only necessary information for the reconstruction of $X$ can pass. I tried to implement this in the simplest from so that Information Bottleneck can be easily leveraged as a regularizer or metric for other projects.

• $X$ is the input,
• $Y$ is the label,
• We look for a latent variable $Z$ that maximizes the mutual information $I(Z;Y)$, meanwhile, it has to minimize $I(Z;X)$.
• For more detials and theoritical proofs please check https://arxiv.org/abs/1612.00410

python VIBV4.py