Generative Adversarial Networks: Applications, Challenges, and Open Issues

Abstract

Generative Adversarial Networks (GANs) represent an emerging class of deep generative models that have been attracting notable interest in recent years. These networks are unique in their capacity to train high-dimensional distributions spanning a range of data types. Conventional GANs encounter problems related to model collapse, convergence, and instability. These issues can be primarily attributed to suboptimal network architecture design, misuse of objective functions, and inappropriate parameter optimisation methods. Several studies have made efforts to tackle these issues, to varying degrees of success. This research aims to offer an exhaustive review of contemporary techniques utilised in GANs, the persisting problems they face, applications of these techniques and performance evaluation metrics across various sectors. Comprehensive searches were performed using selected publications from 2014 to 2022 and out of 260 publications retrieved, 20 publications (7.69%) were deemed eligible. The result using Comprehensive Meta-Analysis (CMA) tool shows the mean effect size is −0,537 with a 95% confidence interval of −1205 to 0,132 having a p-value >0.05. This analysis will equip researchers with deeper insights into the potential applications of GANs and how they can help address current challenges in various domains.