A Detailed Study on Generative Adversarial Networks
Shailender Kumar, Sumit Dhawan · 2020
From past decades, along with increase in computing power, different generative models have also been developed in the area of machine learning. Among all such generative models, one very popular generative model, called as Generative Adversarial Networks, has been introduced and researched upon, in past few years only. It is based on the concept of two adversaries constantly trying to outperform each other. Objective of this review is to extensively study the GAN related literature and provide a summarized form of the studied literature available on GAN, including the concept behind it, its objective function, proposed modifications in base model, and recent trends in this field. This paper will help in giving a thorough understating of GAN. This paper will give an overview of GAN and discuss the popular variants of this model, its common applications, different evaluation metrics proposed for it, and finally its drawbacks, conclusion of the paper and future course of action.