  We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable
approach to generative modeling which builds on the stale (time-independent)
approximation of the gradient flow of entropy-regularized f-divergences
introduced in recent work. Specifically, the intractable time-dependent density
ratio is approximated by a stale estimator given by a GAN discriminator. This
is sufficient in the case of sample refinement, where the source and target
distributions of the flow are close to each other. However, this assumption is
invalid for generation and a naive application of the stale estimator fails due
to the large chasm between the two distributions. FDRL proposes to train a
density ratio estimator such that it learns from progressively improving
samples during the training process. We show that this simple method alleviates
the density chasm problem, allowing FDRL to generate images of dimensions as
high as $128\times128$, as well as outperform existing gradient flow baselines
on quantitative benchmarks. We also show the flexibility of FDRL with two use
cases. First, unconditional FDRL can be easily composed with external
classifiers to perform class-conditional generation. Second, FDRL can be
directly applied to unpaired image-to-image translation with no modifications
needed to the framework. Our code is publicly available at
ttps://github.com/clear-nus/fdrl.
