Perfect density models cannot guarantee anomaly detection

Abstract

Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning. However, the likelihood values empirically attributed to anomalies conflict with the expectations these proposed applications suggest. In this paper, we take a closer look at the behavior of distribution densities through the lens of reparametrization and show that these quantities carry less meaningful information than previously thought, beyond estimation issues or the curse of dimensionality. We conclude that the use of these likelihoods for anomaly detection relies on strong and implicit hypotheses, and highlight the necessity of explicitly formulating these assumptions for reliable anomaly detection.

Accepted to the Special Issue "Probabilistic Methods for Deep Learning" of the Journal Entropy. 14 pages and 10 figures in main content, 4 pages of bibliography, and 2 pages in Appendix

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• Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping
• Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
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• Anomaly detection with flow-based fast calorimeter simulators
• Object classification on video data of meteors and meteor-like phenomena: algorithm and data
• Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
• New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
• Autoencoding Under Normalization Constraints
• Entropic Issues in Likelihood-Based OOD Detection