@inproceedings{
fang2024kernel,
title={Kernel {PCA} for Out-of-Distribution Detection},
author={Kun Fang and Qinghua Tao and Kexin Lv and Mingzhen He and Xiaolin Huang and JIE YANG},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=EZpKBC1ohS}
}
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 non-linear mappings.
In this work, we leverage the framework of Kernel PCA (KPCA) for OoD detection, and seek suitable non-linear kernels that advocate the separability between InD and OoD data in the subspace spanned by the principal components.
Besides, explicit feature mappings induced from the devoted task-specific kernels are adopted so that the KPCA reconstruction error for new test samples can be efficiently obtained with large-scale data.
Extensive theoretical and empirical results on multiple OoD data sets and network structures verify the superiority of our KPCA detector in  efficiency and efficacy with state-of-the-art detection performance.
Kernel PCA for Out-of-Distribution Detection
Kun Fang Qinghua Tao Kexin Lv Mingzhen He Xiaolin Huang JIE YANG