<ul><li>Researchers propose a novel technique to enhance out-of-distribution (OOD) detection in pre-trained deep learning networks without altering their original parameters.</li><li>The approach defines probabilistic trust intervals for each network weight using in-distribution data and samples additional weight values during inference.</li><li>By quantifying the disagreements among outputs, the method achieves improved OOD detection performance compared to various baseline methods.</li><li>The proposed approach demonstrates robustness in identifying corrupted and adversarial inputs, without requiring OOD samples during training.</li></ul>

Probabilistic Trust Intervals for Out of Distribution Detection

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