Cerebellum-Inspired Kernel for Robust OOD Detection

Abstract

Detecting novel stimuli is a fundamental neural function, yet its machine learning counterpart--out-of-distribution (OOD) detection--remains challenging, with models often making overconfident predictions on unseen inputs. Inspired by the strong pattern-separation capabilities of cerebellum-like circuits, we introduce a cerebellum-inspired kernel with an efficient closed-form implementation. Combining random Gaussian projection with Top-k sparsification, the kernel reshapes similarities in high-dimensional space to enhance separability between in-distribution (ID) and OOD samples. On OpenOOD benchmarks, our kernel consistently improves multiple baseline methods, and pairing it with the energy score achieves performance comparable to or exceeding current state-of-the-art approaches. The closed-form design also avoids the high computational cost of large-expansion explicit mapping. These results demonstrate the generality and potential of cerebellar kernels for OOD detection and other tasks requiring efficient pattern separation under limited computational resources.