# The Variational InfoMax AutoEncoder

## TL;DR

This paper introduces the Variational InfoMax (VIM), a new learning objective for VAEs that optimizes both inference and generative models while controlling network capacity to improve informativeness and robustness.

## Contribution

The paper proposes the VIM objective, which simultaneously learns inference and generative models with explicit capacity control, addressing limitations of the ELBO in VAEs.

## Key findings

- VIM improves the informativeness of the generator.
- VIM provides explicit capacity estimation.
- VIM enhances network robustness.

## Abstract

The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative generator. In order to solve such an issue, we provide a learning objective, learning a maximal informative generator while maintaining bounded the network capacity: the Variational InfoMax (VIM). The contribution of the VIM derivation is twofold: an objective learning both an optimal inference and generative model and the explicit definition of the network capacity, an estimation of the network robustness.