Enhancing the stability of Generative Adversarial Networks: A survey of progress and techniques

This survey aims to comprehensively review and analyze the progress in enhancing the stability of generative adversarial networks (GANs). It systematically explores the evolution of GAN architectures, loss functions, and regularization techniques, focusing on their impact on training stability. In addition, this article briefly discusses the main challenges encountered during GAN training, such as mode collapse and vanishing/exploding gradients, and synthesizes various strategies and methods developed to alleviate these problems. Finally, the survey highlights the ongoing need for innovative solutions to improve the stability of GAN training and ensure its effective and robust application in various fields.