Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate samples from the data distribution are then obtained by estimating the time-reversal of this diffusion using score matching ideas. We follow here a similar strategy to sample from unnormalized probability densities and compute their normalizing constants. However, the time-reversed diffusion is here simulated by using an original iterative particle scheme relying on a novel score matching loss. Contrary to standard denoising diffusion models, the resulting Particle Denoising Diffusion Sampler (PDDS) provides asymptotically consistent estimates under mild assumptions. We demonstrate PDDS on multimodal and high dimensional sampling tasks.

翻译：去噪扩散模型已广泛应用于生成建模。其核心思想是通过扩散过程将数据分布转化为高斯分布。随后通过得分匹配技术估计该扩散的时间反转过程，从而获得数据分布的近似样本。本文采用类似策略对未归一化概率密度进行采样，并计算其归一化常数。然而，时间反转扩散过程在此通过一种基于新型得分匹配损失的原创迭代粒子方案进行模拟。与标准去噪扩散模型不同，所提出的粒子去噪扩散采样器（PDDS）在温和假设下提供渐近一致的估计结果。我们在多模态和高维采样任务上验证了PDDS的性能。