  Deep generative modeling has seen impressive advances in recent years, to the
point where it is now commonplace to see simulated samples (e.g., images) that
closely resemble real-world data. However, generation quality is generally
inconsistent for any given model and can vary dramatically between samples. We
introduce Discriminator Gradient flow (DGflow), a new technique that improves
generated samples via the gradient flow of entropy-regularized f-divergences
between the real and the generated data distributions. The gradient flow takes
the form of a non-linear Fokker-Plank equation, which can be easily simulated
by sampling from the equivalent McKean-Vlasov process. By refining inferior
samples, our technique avoids wasteful sample rejection used by previous
methods (DRS & MH-GAN). Compared to existing works that focus on specific GAN
variants, we show our refinement approach can be applied to GANs with
vector-valued critics and even other deep generative models such as VAEs and
Normalizing Flows. Empirical results on multiple synthetic, image, and text
datasets demonstrate that DGflow leads to significant improvement in the
quality of generated samples for a variety of generative models, outperforming
the state-of-the-art Discriminator Optimal Transport (DOT) and Discriminator
Driven Latent Sampling (DDLS) methods.
