Abstract
In the last few years, a type of generative model known as Generative Adversarial Networks (GANs), has achieved tremendous success mainly in the field of computer vision, image classification, speech and language processing, etc. GANs are the models which are used to produce new samples which have similar data distribution as of the training dataset. In this review paper, we will first introduce the idea behind the GANs, followed by a brief overview of various types of GANs as well as comparing it with different generative models. Then, we will discuss the application range and finally the future work with its associated research frontiers.

view publication