Generative Adversarial Networks (GANs): A Review of Theory, Applications, and Future Directions

Abstract

Generative Adversarial Networks (GANs) have emerged as one of the most significant advancements in the field of machine learning and artificial intelligence since their introduction by Ian Goodfellow and colleagues in 2014. GANs have revolutionized the way we approach generative modeling, offering a novel framework for training generative models through an adversarial process. This paper provides a comprehensive review of the theoretical foundations of GANs, their diverse applications across various domains, and the future directions that research in this area might take. We begin by discussing the fundamental concepts and mathematical underpinnings of GANs, followed by an exploration of their applications in image synthesis, video generation, text-to-image synthesis, and more. We also delve into the challenges and limitations associated with GANs, including mode collapse, training instability, and ethical concerns. Finally, we outline potential future research directions, including the development of more stable training methods, the exploration of GANs in new domains, and the integration of GANs with other machine learning paradigms. This review aims to provide a thorough understanding of GANs, their current state, and their potential for future advancements.