mlsanityData to 7 Oct 2026

Advancing Out-of-Distribution Detection via Local Neuroplasticity

arXiv 2502.15833 ↗First posted 20 Feb 2025cs.LG cs.AI cs.CVcited by 1

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Abstract (arXiv)

In the domain of machine learning, the assumption that training and test data share the same distribution is often violated in real-world scenarios, requiring effective out-of-distribution (OOD) detection. This paper presents a novel OOD detection method that leverages the unique local neuroplasticity property of Kolmogorov-Arnold Networks (KANs). Unlike traditional multilayer perceptrons, KANs exhibit local plasticity, allowing them to preserve learned information while adapting to new tasks. Our method compares the activation patterns of a trained KAN against its untrained counterpart to detect OOD samples. We validate our approach on benchmarks from image and medical domains, demonstrating superior performance and robustness compared to state-of-the-art techniques. These results underscore the potential of KANs in enhancing the reliability of machine learning systems in diverse environments.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 44 of its 74 references that are in the map. First posted on arXiv 20 Feb 2025.

Builds on

44 of 74 references.

References · 44

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32 more
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Citing papers per year: 2025: 1.

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  1. 2025Automatic Grid Updates for Kolmogorov-Arnold Networks using Layer Histogramscited by 3