  Probability density estimation is a classical and well studied problem, but
standard density estimation methods have historically lacked the power to model
complex and high-dimensional image distributions. More recent generative models
leverage the power of neural networks to implicitly learn and represent
probability models over complex images. We describe methods to extract explicit
probability density estimates from GANs, and explore the properties of these
image density functions. We perform sanity check experiments to provide
evidence that these probabilities are reasonable. However, we also show that
density functions of natural images are difficult to interpret and thus limited
in use. We study reasons for this lack of interpretability, and show that we
can get interpretability back by doing density estimation on latent
representations of images.
