# Biadversarial Variational Autoencoder

## TL;DR

This paper introduces a Biadversarial Variational Autoencoder that replaces Gaussian assumptions with adversarial networks, enabling better modeling of multimodal distributions and improving image quality.

## Contribution

It proposes a novel VAE framework using adversarial networks to avoid Gaussian assumptions, enhancing the ability to model complex, multimodal data distributions.

## Key findings

- Avoids Gaussian assumptions in VAE
- Improves modeling of multimodal distributions
- Produces sharper, higher-quality images

## Abstract

In the original version of the Variational Autoencoder, Kingma et al. assume Gaussian distributions for the approximate posterior during the inference and for the output during the generative process. This assumptions are good for computational reasons, e.g. we can easily optimize the parameters of a neural network using the reparametrization trick and the KL divergence between two Gaussians can be computed in closed form. However it results in blurry images due to its difficulty to represent multimodal distributions. We show that using two adversarial networks, we can optimize the parameters without any Gaussian assumptions.