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arXiv · 2112.05341

Hyperdimensional Feature Fusion for Out-Of-Distribution Detection

Samuel Wilson·Tobias Fischer·Niko Sünderhauf·Feras Dayoub
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Abstract

We introduce powerful ideas from Hyperdimensional Computing into the challenging field of Out-of-Distribution (OOD) detection. In contrast to most existing work that performs OOD detection based on only a single layer of a neural network, we use similarity-preserving semi-orthogonal projection matrices to project the feature maps from multiple layers into a common vector space. By repeatedly applying the bundling operation $\oplus$, we create expressive class-specific descriptor vectors for all in-distribution classes. At test time, a simple and efficient cosine similarity calculation between descriptor vectors consistently identifies OOD samples with better performance than the current state-of-the-art. We show that the hyperdimensional fusion of multiple network layers is critical to achieve best general performance.

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Samuel Wilson, Tobias Fischer, Niko Sünderhauf, Feras Dayoub. 2021-12-10. Hyperdimensional Feature Fusion for Out-Of-Distribution Detection. https://arxiv.org/abs/2112.05341

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