Abstract
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 uses data from a normal distribution while the generator from a gaussian distribution. The combination from both is given to a discriminator which tells whether the generated images are correct or not. We evaluate our network on 3 different datasets: MNIST, LSUN and CelebA-HQ 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.
Abstract (translated)
URL
https://arxiv.org/abs/2008.10399
