Revisit PCA-based technique for Out-of-Distribution Detection

Abstract

Out-of-distribution (OOD) detection is a desired ability to ensure the reliability and safety of intelligent systems. A scoring function is often designed to measure the degree of any new data being an OOD sample. While most designed scoring functions are based on a single source of information (e.g., the classifier’s output, logits, or feature vector), recent studies demonstrate that fusion of multiple sources may help better detect OOD data. In this study, after detailed analysis of the issue in OOD detection by the conventional principal component analysis (PCA), we propose fusing a simple regularized PCA-based reconstruction error with other source of scoring function to further improve OOD detection performance. In particular, when combined with a strong energy score-based OOD method, the regularized reconstruction error helps achieve new state-of the-art OOD detection results on multiple standard benchmarks. The code is available at https://github.com/SYSUMIA-GROUP/pca-based-out-of-distribution-detection.