  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.
