FIS-GAN: GAN with Flow-based Importance Sampling

6 Oct 2019Shiyu YiDonglin ZhanZhengyang GengWenqing ZhangChang Xu

Generative Adversarial Networks (GAN) training process, in most cases, apply uniform and Gaussian sampling methods in 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 gradient algorithms for supervised learning by prioritizing training examples... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.