Subject Review: Generative Adversarial Networks from Architectural Foundations to Future Trajectories

Abstract

Generative Adversarial Networks (GANs) have radically transformed the field of artificial intelligence by offering a highly effective model for understanding complex data distributions and creating new, high-quality content. The evolution of the sector has been incredibly rapid since their invention, resulting in advanced architectural frameworks, highly efficient training techniques, and numerous applications. This review delivers an in-depth and up- to-date overview of the GANs environment, principally highlighting the progress made over 2020 and beyond. We kick off by presenting the basic adversarial concept and the problems that come along with it. Next, through a systematic approach, we depict and discuss the progress of the GANs structures, starting with the superior output of StyleGAN, going through the amalgamation of transformers and 3D data studies, and so on. An enormous part of this paper is invested in presenting a compact, yet extensive, panorama of the most advanced applications found across computer vision, healthcare, data science, and other novel areas. Moreover, we take a deep and critical dive into persistent issues like evaluation, mode collapse, and theoretical understanding, their challenges, and the ways in which recent works have tried to solve them. To wrap up, we consider the onward path of GANs in light of the impressive proliferation of diffusion models and propose the idea of hybrid generative systems in the future while emphasizing the oppositional approach's extraordinary and enduring features.