arXiv AI By Maria Stoica, Francesco Leofante, Alessio Lomuscio

Out-of-Distribution Detection using Counterfactual Distance

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arXiv Machine Learning
Jul 21

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

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.

By Fengqiang Wan, Qing-Yuan Jiang, Fu Shen, Yang Yang