What is a Generative Adversarial Network (GAN)?

Answer

A GAN consists of two neural networks trained in a minimax game: the Generator (G) tries to create realistic fake data to fool the discriminator; the Discriminator (D) tries to distinguish real data from fake. G and D are trained alternately — as D gets better at detecting fakes, G improves at generating realistic samples. GANs can generate photorealistic images (StyleGAN), create deepfakes, perform image-to-image translation (Pix2Pix, CycleGAN), and super-resolution. Training challenges include mode collapse and training instability.