  Deep energy-based models (EBMs) are very flexible in distribution
parametrization but computationally challenging because of the intractable
partition function. They are typically trained via maximum likelihood, using
contrastive divergence to approximate the gradient of the KL divergence between
data and model distribution. While KL divergence has many desirable properties,
other f-divergences have shown advantages in training implicit density
generative models such as generative adversarial networks. In this paper, we
propose a general variational framework termed f-EBM to train EBMs using any
desired f-divergence. We introduce a corresponding optimization algorithm and
prove its local convergence property with non-linear dynamical systems theory.
Experimental results demonstrate the superiority of f-EBM over contrastive
divergence, as well as the benefits of training EBMs using f-divergences other
than KL.
