Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data instances that resemble existing data. They consist of two neural networks, a generator and a discriminator, which work against each other in a game-theoretic scenario. The generator creates synthetic data, while the discriminator evaluates its authenticity against real data. This process continues until the generator produces data that is indistinguishable from real data, making GANs particularly useful in fields such as image generation, video creation, and data augmentation. Common applications include creating realistic images, enhancing low-resolution images, and generating art or music.