Spectrum of Advancements and Developments in Multidisciplinary Domains for Generative Adversarial Networks (GANs)

Abstract

The survey paper summarizes the recent applications and developments in the domain of Generative Adversarial Networks (GANs) i.e. a back propagation based neural network architecture for generative modeling. GANs is one of the most highlighted research avenue due to its synthetic data generation capabilities and benefits of representations comprehended irrespective of the application. While several reviews for GANs in the arena of image processing have been conducted by present but none have given attention on the review of GANs over multi-disciplinary domains. Therefore, in this survey, use of GAN in multidisciplinary applications areas and its implementation challenges have been done by conducting a rigorous search for journal/research article related to GAN and in this regard five renowned journal databases i.e. “ACM Digital Library”,” Elsevier”, “IEEE Explore”, “Science Direct”, “Springer” and proceedings of best domain specific conference are considered. By employing hybrid research methodology and article inclusion and exclusion criteria, 100 research articles are considered encompassing 23 application domains for the survey. In this paper applications of GAN in various practical domain and their implementation challenges its associated advantages and disadvantages have been discussed. For the first time a survey of this type have been done where GAN with wide range of application and its associated advantages and disadvantages issue have been reviewed. Finally, this article presents several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing GANs related research and eventually help to develop an intuition for problem solving using GANs.