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 drives both networks to improve, ultimately leading the generator to produce highly realistic outputs. GANs have become a foundational technique in AI for generating images, videos, and other types of data.