  Among the wide variety of image generative models, two models stand out:
Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs
can produce realistic images, but they suffer from mode collapse and do not
provide simple ways to get the latent representation of an image. On the other
hand, VAEs do not have these problems, but they often generate images less
realistic than GANs. In this article, we explain that this lack of realism is
partially due to a common underestimation of the natural image manifold
dimensionality. To solve this issue we introduce a new framework that combines
VAE and GAN in a novel and complementary way to produce an auto-encoding model
that keeps VAEs properties while generating images of GAN-quality. We evaluate
our approach both qualitatively and quantitatively on five image datasets.
