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:深层空间以能源为基础的模型(EBM)也被称为以能源为基础的前身,在基因模型方面引起了越来越多的兴趣。由于在潜在空间的构思方面的灵活性和强大的建模能力,最近基于这一模型的工程做出了令人感兴趣的尝试,目的是解释文本模型的可解释性;然而,潜层空间EBM也继承了数据空间EBM的一些缺陷;实践中的低劣MCMC取样质量会导致培训质量差和不稳定,特别是复杂潜质结构数据的培训。由于最近努力利用扩散回收可能性学习作为取样问题的解药,我们把扩散模型与潜在空间EBM之间的新型共生关系引入一个变异学习框架中,作为潜在的扩散能源基模型。我们与信息瓶颈一起开发了基于几何集群的正规化,以进一步提高所学过的潜在空间的质量。关于若干具有挑战性的任务的实验表明,我们关于可解释的文本模型优于强大的对应方。