Classification-oriented adaptive sensing via posterior sampling
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 dom...
| Authors | Andriy Enttsel, Maxime Rousselot, Vincent Corlay |
|---|---|
| Primary category | eess.SP |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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