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Why is the Mahalanobis Distance Effective for Anomaly Detection?
[article]
2020
arXiv
pre-print
The Mahalanobis distance-based confidence score, a recently proposed anomaly detection method for pre-trained neural classifiers, achieves state-of-the-art performance on both out-of-distribution (OoD) and adversarial examples detection. This work analyzes why this method exhibits such strong performance in practical settings while imposing an implausible assumption; namely, that class conditional distributions of pre-trained features have tied covariance. Although the Mahalanobis
arXiv:2003.00402v2
fatcat:jeackg3wxbhqpa7ew6h2uwh5bm