GAN (Generative Adversarial Networks)

Generative Adversarial Networks (GANs) are a type of artificial intelligence system that consists of two parts: a "generator" and a "discriminator." The generator creates new data—like images or sounds—aiming to mimic real examples, while the discriminator evaluates whether the data looks genuine or fake. They train together in a game: the generator improves to fool the discriminator, and the discriminator gets better at spotting fakes. Over time, this process produces highly realistic synthetic data that can resemble real-world content, making GANs powerful tools for tasks like image synthesis, augmentation, and creative design.