Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models. Two networks, a generator and a discriminator, are trained in an adversarial manner. The generator creates new data instances, while the discriminator evaluates them for authenticity. Through this competitive process, the generator learns to produce increasingly realistic data, and the discriminator becomes better at distinguishing real from fake data. This approach has shown significant promise in generating various types of data, including images, and has become a foundational technique in AI-driven design and content creation.