Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Empirical Comparison of GANs and Normalizing Flows for Density
  Estimation   Generative adversarial networks (GANs) and normalizing flows are both
approaches to density estimation that use deep neural networks to transform
samples from an uninformative prior distribution to an approximation of the
data distribution. There is great interest in both for general-purpose
statistical modeling, but the two approaches have seldom been compared to each
other for modeling non-image data. The difficulty of computing likelihoods with
GANs, which are implicit models, makes conducting such a comparison
challenging. We work around this difficulty by considering several
low-dimensional synthetic datasets. An extensive grid search over GAN
architectures, hyperparameters, and training procedures suggests that no GAN is
capable of modeling our simple low-dimensional data well, a task we view as a
prerequisite for an approach to be considered suitable for general-purpose
statistical modeling. Several normalizing flows, on the other hand, excelled at
these tasks, even substantially outperforming WGAN in terms of Wasserstein
distance -- the metric that WGAN alone targets. Scientists and other
practitioners should be wary of relying on WGAN for applications that require
accurate density estimation.

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The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph