Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient Normalization for Generative Adversarial Networks   In this paper, we propose a novel normalization method called gradient
normalization (GN) to tackle the training instability of Generative Adversarial
Networks (GANs) caused by the sharp gradient space. Unlike existing work such
as gradient penalty and spectral normalization, the proposed GN only imposes a
hard 1-Lipschitz constraint on the discriminator function, which increases the
capacity of the discriminator. Moreover, the proposed gradient normalization
can be applied to different GAN architectures with little modification.
Extensive experiments on four datasets show that GANs trained with gradient
normalization outperform existing methods in terms of both Frechet Inception
Distance and Inception Score.

4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys