  We study a new approach to learning energy-based models (EBMs) based on
adversarial training (AT). We show that (binary) AT learns a special kind of
energy function that models the support of the data distribution, and the
learning process is closely related to MCMC-based maximum likelihood learning
of EBMs. We further propose improved techniques for generative modeling with
AT, and demonstrate that this new approach is capable of generating diverse and
realistic images. Aside from having competitive image generation performance to
explicit EBMs, the studied approach is stable to train, is well-suited for
image translation tasks, and exhibits strong out-of-distribution adversarial
robustness. Our results demonstrate the viability of the AT approach to
generative modeling, suggesting that AT is a competitive alternative approach
to learning EBMs.
