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 samples and a discriminator that tries to distinguish between real and generated samples. Through this adversarial process, both networks improve, leading to the generator producing increasingly realistic outputs. This approach has shown great promise in generating images, videos, and other types of data.