GAN Framework

Generative Adversarial Networks (GANs) are a deep learning framework designed to generate synthetic data resembling real data by pitting a generator network against a discriminator network in a competitive game. Current research focuses on improving GAN training stability and addressing challenges like mode collapse and ensuring data quality, exploring variations such as diffusion-based GANs and those incorporating privacy-preserving techniques like differential privacy. These advancements are impacting diverse fields, from enhancing data augmentation for tasks like road damage detection and radio signal modeling to enabling responsible data synthesis for privacy-sensitive applications.