  Generative Adversarial Networks (GAN) training process, in most cases, apply
Uniform or Gaussian sampling methods in the latent space, which probably spends
most of the computation on examples that can be properly handled and easy to
generate. Theoretically, importance sampling speeds up stochastic optimization
in supervised learning by prioritizing training examples. In this paper, we
explore the possibility of adapting importance sampling into adversarial
learning. We use importance sampling to replace Uniform and Gaussian sampling
methods in the latent space and employ normalizing flow to approximate latent
space posterior distribution by density estimation. Empirically, results on
MNIST and Fashion-MNIST demonstrate that our method significantly accelerates
GAN's optimization while retaining visual fidelity in generated samples.
