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.
Figures & tables
Fig. 1: Posterior sampling-based adaptive sensing for reconstruction (blue) and classification (red). The shared blocks are in green.
Fig. 2: Classification accuracy and reconstruction PSNR at the adaptive stopping time on MNIST (top) and CIFAR-10 (bottom).
Fig. 3: Posterior sample-size ablation in the high-accuracy regions of Fig. 2 .