From Scratch - Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a method in generative AI that aims to train a Generator (G) model and a Discriminator (D) model simultaneously. The G model learns to generate samples from a given distribution, while the D model learns to distinguish between real and generated samples. The training regime involves updating the D model to maximize the probability of correct classification, and updating the G model to maximize the probability of the D model making a mistake. The Discriminator model has 4 linear layers with dropout and ReLU activations.