Flow-based Deep Generative Models

So far, I’ve written about two types of generative models, GAN and VAE. Neither of them explicitly learns the probability density function of real data, $p(mathbf{x})$ (where $mathbf{x} in mathcal{D}$) — because it is really hard! Taking the generative model with latent variables as an example, $p(mathbf{x}) = int p(mathbf{x}vertmathbf{z})p(mathbf{z})dmathbf{z}$ can hardly be calculated as it is intractable to go through all possible values of the latent code $mathbf{z}$.