  Generative models have shown strong generation ability while efficient
likelihood estimation is less explored. Energy-based models~(EBMs) define a
flexible energy function to parameterize unnormalized densities efficiently but
are notorious for being difficult to train. Adversarial EBMs introduce a
generator to form a minimax training game to avoid expensive MCMC sampling used
in traditional EBMs, but a noticeable gap between adversarial EBMs and other
strong generative models still exists. Inspired by diffusion-based models, we
embedded EBMs into each denoising step to split a long-generated process into
several smaller steps. Besides, we employ a symmetric Jeffrey divergence and
introduce a variational posterior distribution for the generator's training to
address the main challenges that exist in adversarial EBMs. Our experiments
show significant improvement in generation compared to existing adversarial
EBMs, while also providing a useful energy function for efficient density
estimation.
