GAN Structure

Generative Adversarial Networks (GANs) are increasingly used to generate and manipulate data across diverse scientific domains. Current research focuses on enhancing GAN architectures to address specific challenges, such as improving parameter estimation accuracy for complex distributions, generating robust adversarial examples for improved security in image recognition, and controlling the quality of generated images for applications like image restoration and manipulation. These advancements are impacting fields ranging from telecommunications and computer vision to gravitational wave physics and precision agriculture, enabling more accurate modeling, robust security measures, and improved data analysis capabilities.