Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: G2R Bound: A Generalization Bound for Supervised Learning from
  GAN-Synthetic Data   Performing supervised learning from the data synthesized by using Generative
Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important
applications. First, GANs may generate more labeled training data, which may
help improve classification accuracy. Second, in scenarios where real data
cannot be released outside certain premises for privacy and/or security
reasons, using GAN- synthetic data to conduct training is a plausible
alternative. This paper proposes a generalization bound to guarantee the
generalization capability of a classifier learning from GAN-synthetic data.
This generalization bound helps developers gauge the generalization gap between
learning from synthetic data and testing on real data, and can therefore
provide the clues to improve the generalization capability.

6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math