Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Empirical Analysis of Overfitting and Mode Drop in GAN Training   We examine two key questions in GAN training, namely overfitting and mode
drop, from an empirical perspective. We show that when stochasticity is removed
from the training procedure, GANs can overfit and exhibit almost no mode drop.
Our results shed light on important characteristics of the GAN training
procedure. They also provide evidence against prevailing intuitions that GANs
do not memorize the training set, and that mode dropping is mainly due to
properties of the GAN objective rather than how it is optimized during
training.

36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph