  In this work, we present a novel neural network to generate high resolution
images. We replace the decoder of VAE with a discriminator while using the
encoder as it is. The encoder is fed data from a normal distribution while the
generator is fed from a gaussian distribution. The combination from both is
given to a discriminator which tells whether the generated image is correct or
not. We evaluate our network on 3 different datasets: MNIST, LSUN and CelebA
dataset. Our network beats the previous state of the art using MMD, SSIM, log
likelihood, reconstruction error, ELBO and KL divergence as the evaluation
metrics while generating much sharper images. This work is potentially very
exciting as we are able to combine the advantages of generative models and
inference models in a principled bayesian manner.
