  Variational auto-encoders (VAEs) provide an attractive solution to image
generation problem. However, they tend to produce blurred and over-smoothed
images due to their dependence on pixel-wise reconstruction loss. This paper
introduces a new approach to alleviate this problem in the VAE based generative
models. Our model simultaneously learns to match the data, reconstruction loss
and the latent distributions of real and fake images to improve the quality of
generated samples. To compute the loss distributions, we introduce an
auto-encoder based discriminator model which allows an adversarial learning
procedure. The discriminator in our model also provides perceptual guidance to
the VAE by matching the learned similarity metric of the real and fake samples
in the latent space. To stabilize the overall training process, our model uses
an error feedback approach to maintain the equilibrium between competing
networks in the model. Our experiments show that the generated samples from our
proposed model exhibit a diverse set of attributes and facial expressions and
scale up to high-resolution images very well.
