What is Generative Adversarial Networks?

Generative Adversarial Networks (GANs) are a class of machine learning frameworks where two neural networks contest with each other in a zero-sum game. One network, the generator, creates new data instances, while the other, the discriminator, evaluates them for authenticity. The generator tries to fool the discriminator, and the discriminator tries to distinguish fake data from real data. Through this adversarial process, both networks improve, with the generator learning to produce increasingly realistic data and the discriminator becoming better at identifying fakes. GANs are commonly used for image generation, image-to-image translation, text-to-image generation, and video generation, and can also be applied to other data types like audio and text.