面向分类的自适应感知：基于后验采样的方法
Classification-oriented adaptive sensing via posterior sampling
- Mitsubishi Electric R&D Centre Europe（三菱电机欧洲研发中心）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出一种面向分类的自适应感知方法，利用扩散后验采样和类条件高斯混合模型分解不确定性，在未测量子空间选择主导感知方向，实验表明可改善分类-测量权衡。
中文摘要 AI 辅助
扩散模型的最新进展使得通过后验采样实现高性能、实例自适应的压缩感知成为可能，而无需进行特定任务的政策训练。现有方法通过最大化总后验信号方差来选择感知探针，因此主要受重建驱动。我们提出了一种分类驱动的扩展，其动机来自类条件高斯混合模型的闭式后验协方差，该协方差可分解为类内不确定性和类间不确定性。利用校准的软分类器输出，我们从扩散后验样本中估计这些不确定性项，并提出一种面向分类的准则，用于在未测量子空间中选择主导感知方向。在MNIST和CIFAR-10上的实验将所得分类准确率、测量成本和重建质量与面向重建的对应方法进行了比较。结果识别了语义后验不确定性产生更有利的分类-测量权衡的机制，并量化了相关的重建成本。
英文摘要
Recent advances in diffusion models have enabled high-performance, instance-adaptive compressed sensing through posterior sampling, without task-specific policy training. Existing methods select sensing probes by maximizing total posterior signal variance and are therefore primarily reconstruction-driven. We introduce a classification-driven extension motivated by the closed-form posterior covariance of a class-conditional Gaussian mixture model, which decomposes into within-class and between-class uncertainty. Using calibrated soft classifier outputs, we estimate these uncertainty terms from diffusion posterior samples and propose a classification-oriented criterion for selecting the dominant sensing direction in the unmeasured subspace. Experiments on MNIST and CIFAR-10 compare the resulting classification accuracy, measurement cost, and reconstruction quality with those of reconstruction-oriented counterparts. The results identify regimes in which semantic posterior uncertainty yields a more favorable classification--measurement trade-off and quantify the associated reconstruction cost.
