The Math Behind GANs

The Math Behind GANs explores the mathematical foundation of Generative Adversarial Networks, detailing the adversarial competition between a generator and discriminator. Using binary cross entropy as the error function, it demonstrates how the generator creates data to fool the discriminator, while the discriminator identifies real versus fake data. The analysis shows that optimal training minimizes the Jensen-Shannon divergence between real and generated data distributions, confirming the goal of having the generator mimic real data closely.