Data Wrangling

10Generative Adversarial Networks: A Comprehensive Review

Jyoti Arora¹*, Meena Tushir², Pooja Kherwa³ and Sonia Rathee³

¹Department of Information Technology, Maharaja Surajmal Institute of Technology, GGSIPU, New Delhi, India

²Department of Electronics and Electrical Engineering, Maharaja Surajmal Institute of Technology, GGSIPU, New Delhi, India

³Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, GGSIPU, New Delhi, India

Abstract

Generative Adversarial Networks (GANs) have gained immense popularity since their introduction in 2014. It is one of the most popular research area right now in the field of computer science. GANs are arguably one of the newest yet most powerful deep learning techniques with applications in several fields. GANs can be applied to areas ranging from image generation to synthetic drug synthesis. They also find use in video generation, music generation, as well as production of novel works of art. In this chapter, we attempt to present detail study about the GAN and make the topic understandable to the readers of this work. This chapter presents an extensive review of GANs, their anatomy, types, and several applications. We have also discussed the shortcomings of GANs.

Keywords: Generative adversarial networks, learning process, computer vision, deep learning, machine learning

List of Abbreviations

Abbreviation	Full Form
GAN	Generative Adversarial Network
DBM	Deep Boltzmann Machine
DBN	Deep Belief Network
VAE	Variational ...