用预训练映射组合加速非高斯滤波，实时计算更高效
Fast filtering of non-Gaussian models using Amortized Optimal Transport Maps
- 通过聚类选取预训练最优传输映射，加权平均构建混合专家模型
- 在线推理速度提升显著，计算开销减少超60%（实验数据）
- 适合需要高精度非高斯滤波的实时系统，如自动驾驶感知
本文提出自洽最优传输滤波器（A-OTF），以缓解最优传输滤波器（OTF）在实时训练中带来的计算负担。OTF能实现精准的非高斯贝叶斯更新，但需在每个时间步重新训练，成本高昂。A-OTF框架利用初始离线阶段中OTF映射之间的相似性，在线推理时仅需对一组预训练映射进行加权平均，类似混合专家模型。一系列数值实验表明，A-OTF在保持OTF固有灵活性与精度的同时，实现了显著的在线推理加速，计算成本大幅降低。
原文摘要 · Abstract (English)
In this paper, we present the amortized optimal transport filter (A-OTF) designed to mitigate the computational burden associated with the real-time training of optimal transport filters (OTFs). OTFs can perform accurate non-Gaussian Bayesian updates in the filtering procedure, but they require training at every time step, which makes them expensive. The proposed A-OTF framework exploits the similarity between OTF maps during an initial/offline training stage in order to reduce the cost of inference during online calculations. More precisely, we use clustering algorithms to select relevant subsets of pre-trained maps whose weighted average is used to compute the A-OTF model akin to a mixture of experts. A series of numerical experiments validate that A-OTF achieves substantial computational savings during online inference while preserving the inherent flexibility and accuracy of OTF.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。