  Modern implicit generative models such as generative adversarial networks
(GANs) are generally known to suffer from issues such as instability,
uninterpretability, and difficulty in assessing their performance. If we see
these implicit models as dynamical systems, some of these issues are caused by
being unable to control their behavior in a meaningful way during the course of
training. In this work, we propose a theoretically grounded method to guide the
training trajectories of GANs by augmenting the GAN loss function with a
kernel-based regularization term that controls local and global discrepancies
between the model and true distributions. This control signal allows us to
inject prior knowledge into the model. We provide theoretical guarantees on the
stability of the resulting dynamical system and demonstrate different aspects
of it via a wide range of experiments.
