Hyperdimensional Feature Fusion For Out-of-distribution Detection
Samuel Wilson, Tobias Fischer, Niko Sünderhauf, Feras Dayoub . 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021 – 2 citations
[Paper]
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.