Flow-based networks

So far in this chapter, we have studied two kinds of generative models—GANs and VAEs—but there is also another kind, known as flow-based generative models, which directly learn the probability density function of the data distribution, which is something that the previous models do not do. Flow-based models make use of normalizing flows, which overcomes the difficulty that GANs and VAEs face in trying to learn the distribution. This approach can transform a simple distribution into a more complex one through a series of invertible mappings. We repeatedly apply the change of variables rule, which allows the initial probability density to flow through the series of invertible mappings, and at the end, we get the target probability distribution.

Normalizing...