Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding the (un)interpretability of natural image distributions
  using generative models   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.

13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc