GANs

Generative Adversarial Networks (GANs) are a type of artificial intelligence system that learns to create realistic data, such as images or sounds. They consist of two parts: a generator, which produces fake data, and a discriminator, which evaluates whether the data is real or fake. These two parts compete and improve together— the generator refining its creations to fool the discriminator, and the discriminator becoming better at spotting fakes. This adversarial process leads to the generator producing increasingly authentic-looking data, enabling applications like realistic image synthesis, deepfake creation, and data augmentation.