Advancing Out-of-Distribution Detection via Local Neuroplasticity

• URL: http://arxiv.org/abs/2502.15833v1
• Date: Thu, 20 Feb 2025 11:13:41 GMT
• Title: Advancing Out-of-Distribution Detection via Local Neuroplasticity
• Authors: Alessandro Canevaro, Julian Schmidt, Mohammad Sajad Marvi, Hang Yu, Georg Martius, Julian Jordan,
• Abstract summary: This paper presents a novel OOD detection method that leverages the unique local neuroplasticity property of Kolmogorov-Arnold Networks (KANs)<n>Our method compares the activation patterns of a trained KAN against its untrained counterpart to detect OOD samples.<n>We validate our approach on benchmarks from image and medical domains, demonstrating superior performance and robustness compared to state-of-the-art techniques.
• Score: 60.53625435889467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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