Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
Peiyu Yu Yaxuan Zhu Sirui Xie Xiaojian Ma Ruiqi Gao Song-Chun Zhu Ying Nian Wu
Latent space Energy-Based Models (EBMs), also known as energy-based priors,
have drawn growing interests in the field of generative modeling due to its
flexibility in the formulation and strong modeling power of the latent space.
However, the common practice of learning latent space EBMs with non-convergent
short-run MCMC for prior and posterior sampling is hindering the model from
further progress; the degenerate MCMC sampling quality in practice often leads
to degraded generation quality and instability in training, especially with
highly multi-modal and/or high-dimensional target distributions. To remedy this
sampling issue, in this paper we introduce a simple but effective
diffusion-based amortization method for long-run MCMC sampling and develop a
novel learning algorithm for the latent space EBM based on it. We provide
theoretical evidence that the learned amortization of MCMC is a valid long-run
MCMC sampler. Experiments on several image modeling benchmark datasets
demonstrate the superior performance of our method compared with strong
counterparts