Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models by pitting two neural networks against each other: a generator that tries to create realistic data, and a discriminator that tries to distinguish between real and generated data. This adversarial process pushes both networks to improve, ultimately leading the generator to produce highly realistic outputs. This foundational paper introduced the GAN framework, which has since revolutionized the field of AI image generation.