June 5, 2024, 4:45 a.m. | Litian Liu, Yao Qin

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.11536v2 Announce Type: replace
Abstract: Efficient and effective Out-of-Distribution (OOD) detection is essential for the safe deployment of AI systems. Existing feature space methods, while effective, often incur significant computational overhead due to their reliance on auxiliary models built from training features. In this paper, we propose a computationally-efficient OOD detector without using auxiliary models while still leveraging the rich information embedded in the feature space. Specifically, we detect OOD samples based on their feature distances to decision boundaries. To …

arxiv cs.lg decision distribution eess.iv replace type

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