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

翻译：潜空间能量基模型（EBMs），也称为能量先验，因其公式化的灵活性和对潜空间强大的建模能力，在生成式建模领域日益引起关注。然而，现有潜空间EBM通常采用非收敛的短程MCMC进行先验和后验采样，这种常见做法阻碍了模型的进一步发展；在实践中，退化的MCMC采样质量常常导致生成质量下降和训练不稳定，特别是在处理高度多模态和/或高维目标分布时尤为明显。为解决这一采样问题，本文引入一种简单而有效的基于扩散的摊销方法用于长程MCMC采样，并据此开发了一种新颖的潜空间EBM学习算法。我们从理论上证明了所学习的MCMC摊销方法是有效的长程MCMC采样器。在多个图像建模基准数据集上的实验表明，与强基线方法相比，我们的方法具有更优越的性能。