GANs (Generative Adversarial Networks)

Generative Adversarial Networks (GANs) are a type of machine learning model that consist of two parts: a generator and a discriminator. The generator creates new data (like images or sounds), while the discriminator evaluates whether that data is real (from the actual dataset) or fake (produced by the generator). They work together in a game-like process, with the generator improving its creations to fool the discriminator, and the discriminator getting better at spotting fakes. Over time, this competition results in the generator producing highly realistic data, useful for art, design, and various generative tasks.