Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalization Error of GAN from the Discriminator's Perspective   The generative adversarial network (GAN) is a well-known model for learning
high-dimensional distributions, but the mechanism for its generalization
ability is not understood. In particular, GAN is vulnerable to the memorization
phenomenon, the eventual convergence to the empirical distribution. We consider
a simplified GAN model with the generator replaced by a density, and analyze
how the discriminator contributes to generalization. We show that with early
stopping, the generalization error measured by Wasserstein metric escapes from
the curse of dimensionality, despite that in the long term, memorization is
inevitable. In addition, we present a hardness of learning result for WGAN.

29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol