Flow-Based Models

So far, we have discussed a class of deep generative models that model the distribution p(x) directly in an autoregressive manner. The main advantage of ARMs is that they can learn long-range statistics and, as a consequence, powerful density estimators. However, their drawback is that they are parameterized in an autoregressive manner; hence, sampling is rather a slow process. Moreover, they lack a latent representation; therefore, it is not obvious how to manipulate their internal data representation which makes it less appealing for tasks like compression or metric learning. In this chapter, we present a different approach to direct modeling of p(x). However, before we start our considerations, we will discuss a simple example.