arXiv Machine Learning July 21, 2026 By Fengqiang Wan, Qing-Yuan Jiang, Fu Shen, Yang Yang

AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels

Read the original on arXiv Machine Learning →

arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics

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