arXiv Computer Vision September 21, 2026 By Andriy Enttsel, Maxime Rousselot, Vincent Corlay

Classification-oriented adaptive sensing via posterior sampling

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The paper proposes a classification-oriented adaptive sensing method that uses posterior sampling from diffusion models. It leverages the closed-form posterior covariance of a class-conditional Gaussian mixture model to separate within-class and between-class uncertainty, estimating these terms from diffusion posterior samples via calibrated soft classifier outputs. Experiments on MNIST and CIFAR-10 demonstrate that this approach can achieve better classification accuracy for a given measurement cost compared to reconstruction-oriented methods, while also quantifying the associated reconstruction quality.

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