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.

翻译：我们把从超多维计算得出的强大想法引入了具有挑战性的外分布检测领域。与大多数基于神经网络单层进行 OOD检测的现有工作相比,我们使用相似的保存半垂直投影矩阵将地貌图从多层投射到共同矢量空间。我们反复应用捆绑操作 $\oplus, 为所有分布类中的人创建显性级特定描述矢量。在测试时, 描述矢量之间简单而高效的共生相似性计算一致, 一致识别OOOD样本的性能优于目前的最新水平。 我们显示,多网络层的超维融合对于实现最佳总体性能至关重要。