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

本文介绍了一种基于扩散的摊销方法来解决长期 MCMC 采样问题，并基于此开发了一种新的学习算法，用于隐空间 EBM。实验证明，与强对手相比，我们的方法在几个图像建模基准数据集上具有优秀的性能。

用扩散自抑 MCMC 学习基于能量的先验模型

Learning Energy-Based Prior Model with Diffusion-Amortized MCMC

Latent space Energy-Based Models (EBMs), also known as energy-based priors,
have drawn growing interests in generative modeling. Fueled by its flexibility
in the formulation and strong modeling power of the latent space, recent works
built upon it have made interesting attempts aiming at the interpretability of
text modeling. However, latent space EBMs also inherit some flaws from EBMs in
data space; the degenerate MCMC sampling quality in practice can lead to poor
generation quality and instability in training, especially on data with complex
latent structures. Inspired by the recent efforts that leverage diffusion
recovery likelihood learning as a cure for the sampling issue, we introduce a
novel symbiosis between the diffusion models and latent space EBMs in a
variational learning framework, coined as the latent diffusion energy-based
model. We develop a geometric clustering-based regularization jointly with the
information bottleneck to further improve the quality of the learned latent
space. Experiments on several challenging tasks demonstrate the superior
performance of our model on interpretable text modeling over strong
counterparts.

该论文提出了一种新的基于扩散模型与潜空间 EBM 的符号学方法，该方法结合了基于几何聚类的正则化与信息瓶颈，从而在解释性文本建模方面表现出比强大对手更优异的性能。