How do generative adversarial networks (GANs) work?

GANs consist of two neural networks: a generator and a discriminator, trained adversarially. The generator learns to generate realistic data samples, while the discriminator learns to distinguish between real and generated samples. They compete with each other in a min-max game until an equilibrium is reached.