GAN Architecture

Generative Adversarial Networks (GANs) are a class of deep learning models designed to generate new data instances that resemble a training dataset. Current research focuses on improving GAN architectures to address challenges like training instability, mode collapse, and the need for large datasets, exploring variations such as conditional GANs (cGANs), and incorporating techniques like hypernetworks and downstream feedback. These advancements are significantly impacting diverse fields, enabling applications ranging from medical image enhancement and data augmentation to synthetic data generation for improving model fairness and efficiency in computationally intensive simulations.