Diffusion Models and GANs: Mathematics of Generative Visual Networks

Creating high-fidelity imagery dynamically requires modeling complex probability distributions. Generative Adversarial Networks (GANs) achieve this through a zero-sum game between a generator and a discriminator. Meanwhile, Latent Diffusion Models iteratively remove noise from a Gaussian distribution. We break down the mathematical foundations of denoising score-matching and variational autoencoders.