  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.
