  Generative adversarial network (GAN) is a framework for generating fake data
using a set of real examples. However, GAN is unstable in the training stage.
In order to stabilize GANs, the noise injection has been used to enlarge the
overlap of the real and fake distributions at the cost of increasing variance.
The diffusion (or smoothing) may reduce the intrinsic underlying dimensionality
of data but it suppresses the capability of GANs to learn high-frequency
information in the training procedure. Based on these observations, we propose
a data representation for the GAN training, called noisy scale-space (NSS),
that recursively applies the smoothing with a balanced noise to data in order
to replace the high-frequency information by random data, leading to a
coarse-to-fine training of GANs. We experiment with NSS using DCGAN and
StyleGAN2 based on benchmark datasets in which the NSS-based GANs outperforms
the state-of-the-arts in most cases.
