Comparison on Generative Adversarial Networks –A Study

Abstract

Various new deep learning models have been invented, among which generative adversarial networks have gained exceptional prominence in last four years due to its property of image synthesis. GANs have been utilized in diverse fields ranging from conventional areas like image processing, biomedical signal processing, remote sensing, video generation to even off beat areas like sound and music generation. In this paper, we provide an overview of GANs along with its comparison with other networks, as well as different versions of Generative Adversarial Networks.