  Out-of-Distribution (OoD) detection is vital for the reliability of Deep
Neural Networks (DNNs). Existing works have shown the insufficiency of
Principal Component Analysis (PCA) straightforwardly applied on the features of
DNNs in detecting OoD data from In-Distribution (InD) data. The failure of PCA
suggests that the network features residing in OoD and InD are not well
separated by simply proceeding in a linear subspace, which instead can be
resolved through proper nonlinear mappings. In this work, we leverage the
framework of Kernel PCA (KPCA) for OoD detection, seeking subspaces where OoD
and InD features are allocated with significantly different patterns. We devise
two feature mappings that induce non-linear kernels in KPCA to advocate the
separability between InD and OoD data in the subspace spanned by the principal
components. Given any test sample, the reconstruction error in such subspace is
then used to efficiently obtain the detection result with $\mathcal{O}(1)$ time
complexity in inference. Extensive empirical results on multiple OoD data sets
and network structures verify the superiority of our KPCA-based detector in
efficiency and efficacy with state-of-the-art OoD detection performances.
