This paper is published in Volume-5, Issue-2, 2019
Area
Deep Learning
Author
N. Yashwanth
Co-authors
P. Navya, Md. Rukhiya, K. S. V. Prasad, Dr. K. S. Deepthi
Org/Univ
Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India
Pub. Date
14 March, 2019
Paper ID
V5I2-1198
Publisher
Keywords
Generative Adversarial networks, Generator, Discriminator, Neural network, Deep Learning

Citationsacebook

IEEE
N. Yashwanth, P. Navya, Md. Rukhiya, K. S. V. Prasad, Dr. K. S. Deepthi. Survey on generative adversarial networks, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
N. Yashwanth, P. Navya, Md. Rukhiya, K. S. V. Prasad, Dr. K. S. Deepthi (2019). Survey on generative adversarial networks. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
N. Yashwanth, P. Navya, Md. Rukhiya, K. S. V. Prasad, Dr. K. S. Deepthi. "Survey on generative adversarial networks." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

GAN stands for Generative Adversarial Networks.GANs are the most interesting topics in Deep Learning. The concept of GAN is introduced by Ian Good Fellow and his colleagues at the University of Montreal. The main architecture of GAN contains two parts: one is a Generator and the other is Discriminator. The name Adversarial stands for conflict and here the conflict is present between Generator and Discriminator. And hence the name adversarial comes to this concept. In this paper, the author has investigated different ways GAN’s are used in real time applications and what are the different types of GAN’s present.GAN’s are mainly important for generating new data from existing ones. As a machine learning model cannot work properly if the size of the dataset is small GAN’s are here to help to increase the size by creating new fake things from original ones.GAN’s are also used in creating images from the given words that are text-to-image conversion.GANs are also applied in image resolution, image translation and in many other scenarios. From this survey on GAN author aim to know what are the different applications of GAN that are present and their scope. The author has also aimed at knowing the different types of GAN’s available at present. The author has also aimed at knowing the different applications of GAN and different proposed systems by various authors.
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