Advances in Generative Adversarial Networks: From Theory to Practice

Abstract

Generative Adversarial Networks (GANs) have revolutionized the field of machine learning, particularly in the generation of synthetic data, image and video synthesis, and in diverse applications ranging from art creation to drug design. This paper reviews the recent advances in GANs, highlighting key theoretical developments, improvements in training stability, and their growing real-world applications. We discuss the architectural innovations, optimization techniques, and the role of GANs in addressing challenges in data scarcity and simulation. The paper further explores how GANs are being applied in fields such as computer vision, healthcare, and autonomous systems. Finally, we outline the future directions of GAN research and its potential to further transform multiple industries.