Generative Adversarial Networks, or GANs, are a powerful framework in machine learning for generating new data. They consist of two neural networks: a Generator that creates synthetic data from random noise, and a Discriminator that tries to distinguish between real and fake data. These networks are trained simultaneously in an adversarial process, where the Generator tries to fool the Discriminator, and the Discriminator tries to correctly identify real versus fake data.
The training process of GANs involves two alternating steps. First, we train the Discriminator to maximize its ability to correctly classify real data as real and fake data as fake. This is done by using real data with a label of 1, and fake data from the Generator with a label of 0. Second, we train the Generator to create data that can fool the Discriminator. The Generator takes random noise as input and tries to transform it into data that the Discriminator will classify as real. This adversarial process continues until the Generator produces data that is indistinguishable from real data.