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.

通过将能量式模型（EBMs）嵌入到去噪步骤中，将长时间生成的过程分解为几个较小步骤，采用对称的 Jeffrey 散度和引入变分后验分布进行生成器的训练，以解决对抗性 EBMs 存在的主要挑战，实验证明与现有的对抗性 EBMs 相比，在生成方面有显著的改进，同时为高效密度估计提供了有用的能量函数。