Flow-based models for data generation

While both VAEs (Chapter 8, Autoencoders) and GANs do a good job of data generation, they do not explicitly learn the probability density function of the input data. GANs learn by converting the unsupervised problem to a supervised learning problem.

VAEs try to learn by optimizing the maximum log-likelihood of the data by maximizing the Evidence Lower Bound (ELBO). Flow-based models differ from the two in that they explicitly learn data distribution . This offers an advantage over VAEs and GANs, because this makes it possible to use flow-based models for tasks like filling incomplete data, sampling data, and even identifying bias in data distributions. Flow-based models accomplish this by maximizing the log-likelihood estimation. To understand how, let us delve a little into its math.

Let be the probability density of data D, and let be the probability density approximated by our model M. The goal of a flow-based model is to find the...