  Generative adversarial networks (GANs), famous for the capability of learning
complex underlying data distribution, are however known to be tricky in the
training process, which would probably result in mode collapse or performance
deterioration. Current approaches of dealing with GANs' issues almost utilize
some practical training techniques for the purpose of regularization, which on
the other hand undermines the convergence and theoretical soundness of GAN. In
this paper, we propose to stabilize GAN training via a novel particle-based
variational inference -- Langevin Stein variational gradient descent (LSVGD),
which not only inherits the flexibility and efficiency of original SVGD but
aims to address its instability issues by incorporating an extra disturbance
into the update dynamics. We further demonstrate that by properly adjusting the
noise variance, LSVGD simulates a Langevin process whose stationary
distribution is exactly the target distribution. We also show that LSVGD
dynamics has an implicit regularization which is able to enhance particles'
spread-out and diversity. At last we present an efficient way of applying
particle-based variational inference on a general GAN training procedure no
matter what loss function is adopted. Experimental results on one synthetic
dataset and three popular benchmark datasets -- Cifar-10, Tiny-ImageNet and
CelebA validate that LSVGD can remarkably improve the performance and stability
of various GAN models.
