Challenges, Corresponding Solutions, and Applications of Generative Adversarial Networks

Abstract

Generative adversarial networks (GANs) have become a popular deep learning framework in the field of artificial intelligence. Researchers have developed various types of GANs, such as Conditional GANs (CGANs), Mode Dropping GANs (MDGANs), and Wasserstein GANs (WGANs), which have been applied to many different fields. Despite the success of GANs in various tasks, there are still some challenges that need to be addressed. For example, model collapse, non-convergence, and diminished gradient could hinder the training of GANs. In this paper, the authors first introduce the basics of GANs before highlighting a few common problems associated with GANs and their corresponding solutions. For instance, they discuss techniques such as mini-batch discrimination and batch normalization that can help mitigate model collapse and diminish gradient problems. Additionally, they cover methods that can improve the convergence rate and stability of GANs, such as using alternative loss functions and incorporating regularization algorithms. Finally, this paper briefly introduces some recent applications of GANs in image generation, video prediction, and other areas. Despite the challenges that still exist, GANs have shown great promise in various fields, and with further research, GANs have the potential to become more robust and efficient models for generating high-quality synthetic data.