Image Generation u sing Variational Autoencoders

Abstract

The proposed system generates new images from the existing images using variational autoencoders. The autoencoder aims to map the input image to a multivariate normal distribution in the latent space. Variational autoencoder transforms input image into a remarkable output by reducing the reconstruction and KL divergence losses. The primary advantage of implementing variational autoencoder over the other autoencoders is that it follows a specific probability distribution called Gaussian distribution and results in generating high quality images.