Hyperdimensional Feature Fusion for Out-of-Distribution Detection
Date
2023
Authors
Wilson, S.
Fischer, T.
Sunderhauf, N.
Dayoub, F.
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Conference paper
Citation
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023), 2023, pp.2643-2653
Statement of Responsibility
Samuel Wilson, Tobias Fischer, Niko Sünderhauf, Feras Dayoub
Conference Name
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (3 Jan 2023 - 7 Jan 2023 : Waikoloa, Hawaii)
Abstract
We introduce powerful ideas from Hyperdimensional Computing into the challenging field of Out-of-Distribution (OOD) detection. In contrast to most existing works that perform 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 ⊕, 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 competitive performance to the current state-of-the-art whilst being significantly faster. We show that our method is orthogonal to recent state-of-the-art OOD detectors and can be combined with them to further improve upon the performance.
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Date Added to IEEE Xplore: 06 February 2023
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©2023 IEEE