An Overview of Generative Adversarial Networks and Their Variants

Generative Adversarial Networks (GANs) have profoundly advanced the field of generative modeling through a competitive, adversarial training paradigm involving two interconnected neural networks: a generator tasked with synthesizing artificial data samples, and a discriminator designed to critically evaluate their authenticity. This iterative adversarial process effectively enables GANs to produce synthetic outputs of remarkable realism, diversity, and fidelity, driving significant progress across multiple domains and applications. Since their introduction in 2014, GANs have gained significant attention for their applications in image synthesis, semantic editing, style transfer, super-resolution, and beyond. This survey provides a comprehensive overview of GANs, tracing their evolution from the foundational minimax framework to the development of advanced variants such as conditional GANs, Wasserstein GANs, CycleGANs, and StyleGANs. Key theoretical insights, including connections to divergence measures and adversarial optimization, are examined alongside practical considerations like training challenges and solutions. In addition, this work investigates the incorporation of GANs with contemporary deep learning architectures, including Transformers, Physics-Informed Neural Networks (PINNs), and Diffusion Models, shedding light on current trends and novel applications. By outlining key achievements, existing challenges, and prospective research avenues, this survey intends to serve as a comprehensive resource for researchers and practitioners aiming to harness GANs for cutting-edge solutions across various fields.