Advancing Out-of-Distribution Detection via Local Neuroplasticity

Alessandro Canevaro, Julian Schmidt, Mohammad Sajad Marvi, Hang Yu, Georg Martius, Julian Jordan

Keywords: Out-of-Distribution Detection, Local Neuroplasticity, Kolmogorov-Arnold Networks

TL;DR: A novel method leveraging the local neuroplasticity of Kolmogorov-Arnold Networks for OOD detection

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.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 7878