Open AccessUnderstanding Trending Variants of Generative Adversarial Networks
Author(s) -
Tanvi Bhandarkar,
Arvind Murugan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.12.16552
Subject(s) - adversarial system , generative grammar , computer science , field (mathematics) , domain (mathematical analysis) , artificial intelligence , implementation , process (computing) , adaptation (eye) , data science , machine learning , cognitive science , software engineering , mathematics , psychology , mathematical analysis , neuroscience , pure mathematics , operating system
Generative Adversarial Networks (GAN) have its major contribution to the field of Artificial Intelligence. It is becoming so powerful by paving its way in numerous applications of intelligent systems. This is primarily due to its astute prospect of learning and solving complex and high-dimensional problems from the latent space. With the growing demands of GANs, it is necessary to seek its potential and impact in implementations. In short span of time, it has witnessed several variants and extensions in image translation, domain-adaptation and other academic fields. This paper provides an understanding of such imperative GANs mutants and surveys the existing adversarial models which are prominent in their applied field.