Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, data. However, there exist major challenges in training GANs, i.e., mode collapse, non-convergence, instability, due to inappropriate design network architectre, use objective function, selection optimization algorithm. Recently, address these challenges, several solutions for better have been investigated based on techniques re-engineered architectures, new functions, alternative algorithms. To the best our knowledge, no existing survey particularly focused broad systematic developments solutions. In this study, we perform comprehensive advancements proposed handle challenges. We first identify key research issues within each technique then propose taxonomy structure by issues. accordance with taxonomy, provide detailed discussion different variants solution their relationships. Finally, insights gained, present promising directions rapidly growing field.