GAN (Generative Adversarial Network)

GANs consist of a generator that creates fake data and a discriminator that tries to distinguish fake from real. Through this adversarial training, the generator improves until its outputs are indistinguishable from real data. GANs were a breakthrough in image generation before diffusion models and are still used for style transfer, super-resolution, and data augmentation.