Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. The widely-used Denoising Score Matching (DSM) method for scalable EBM training suffers from inconsistency issues, causing the energy model to learn a `noisy' data distribution. In this work, we propose an efficient sampling framework: (pseudo)-Gibbs sampling with moment matching, which enables effective sampling from the underlying clean model when given a `noisy' model that has been well-trained via DSM. We explore the benefits of our approach compared to related methods and demonstrate how to scale the method to high-dimensional datasets.

翻译：能量基模型（EBMs）为复杂数据分布建模提供了灵活的框架。然而，EBMs的训练与采样仍面临重大挑战。广泛使用的可扩展EBM训练方法——去噪分数匹配（DSM）存在不一致性问题，导致能量模型学习到“含噪”的数据分布。在本文中，我们提出了一种高效采样框架：基于矩匹配的（伪）吉布斯采样，当给定一个通过DSM充分训练的“含噪”模型时，该框架能够从底层干净模型中实现有效采样。我们探究了本方法相较于相关方法的优势，并展示了如何将该方法扩展至高维数据集。