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.

翻译：潜在空间能量基模型（EBMs），也称为能量基先验，在生成式建模中引起了越来越多的关注。得益于其公式化的灵活性和潜在空间强大的建模能力，基于该模型的最新工作在文本建模的可解释性方面进行了有趣的尝试。然而，潜在空间EBMs也继承了数据空间EBMs的一些缺陷；实际中退化的MCMC采样质量可能导致生成质量差和训练不稳定，尤其是在具有复杂潜在结构的数据上。受近期利用扩散恢复似然学习作为采样问题解决方案的启发，我们在变分学习框架中引入了扩散模型与潜在空间EBMs之间的新型共生关系，将其命名为潜在扩散能量基模型。我们开发了一种基于几何聚类的正则化方法，并结合信息瓶颈进一步提升所学潜在空间的质量。在多项具有挑战性的任务上的实验表明，我们的模型在可解释文本建模方面优于强大的基线模型。