提出可微分的扩散重采样方法，提升粒子滤波精度与训练效率。
Diffusion differentiable resampling
- 基于无训练扩散模型构建可微重采样机制
- 理论证明分布一致性，实测优于现有方法
- 适合高维图像动态建模的端到端学习
本文研究序列蒙特卡洛（如粒子滤波）中的可微重采样问题。基于重参数化思想，我们提出一种无需训练的扩散模型代理方法，实现信息丰富且即时可微的重采样。理论上证明该方法提供一致的重采样分布；实验表明，在多个滤波与参数估计基准上均优于当前最优可微重采样方法。最终，将其用于高维图像观测下的复杂动态-解码器模型学习，取得了具有竞争力的端到端性能。
原文摘要 · Abstract (English)
This paper is concerned with differentiable resampling in the context of sequential Monte Carlo (e.g., particle filtering). Drawing on reparametrisation, we propose a new resampling method that is informative and instantly differentiable, based on a training-free diffusion model surrogate. We theoretically prove that our diffusion resampling method provides a consistent resampling distribution, and we show empirically that it outperforms the state-of-the-art differentiable resampling methods on multiple filtering and parameter estimation benchmarks. Finally, we show that it achieves competitive end-to-end performance when used in learning a complex dynamics-decoder model with high-dimensional image observations.
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