Advancements and Applications of Generative Adversarial Networks: A Comprehensive Review

Abstract

Abstracts: Our paper offers a comprehensive exploration of Generative Adversarial Networks (GANs), tracing their evolution from Ian Goodfellow's seminal work to their current state-of-the-art status. Delving into the intricacies of GAN architecture and training dynamics, we illuminate their pivotal role in diverse applications such as image synthesis, style transfer, and text-toimage conversion. Through an exhaustive literature review, we dissect the progression of GAN architectures, from Vanilla GANs [1] to advanced variants like Progressive GANs [7] and StyleGANs [8], highlighting their techniques, contributions, and performance across benchmark datasets. Moreover, we confront challenges such as training instability and mode collapse, while also presenting a meticulously curated repository of contemporary generative model advancements. This repository encapsulates the cutting edge of GAN research, showcasing innovative approaches across domains ranging from financial forecasting to image restoration. Despite hurdles and ethical considerations, GANs persist as the vanguard of generative modeling, propelling forward the frontiers of artificial intelligence and creative synthesis.