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This content will become publicly available on June 30, 2023

Title: Unsupervised Anomaly Detection by Robust Density Estimation
Density estimation is a widely used method to perform unsupervised anomaly detection. By learning the density function, data points with relatively low densities are classified as anomalies. Unfortunately, the presence of anomalies in training data may significantly impact the density estimation process, thereby imposing significant challenges to the use of more sophisticated density estimation methods such as those based on deep neural networks. In this work, we propose RobustRealNVP, a deep density estimation framework that enhances the robustness of flow-based density estimation methods, enabling their application to unsupervised anomaly detection. RobustRealNVP differs from existing flow-based models from two perspectives. First, RobustRealNVP discards data points with low estimated densities during optimization to prevent them from corrupting the density estimation process. Furthermore, it imposes Lipschitz regularization to the flow-based model to enforce smoothness in the estimated density function. We demonstrate the robustness of our algorithm against anomalies in training data from both theoretical and empirical perspectives. The results show that our algorithm achieves competitive results as compared to state-of-the-art unsupervised anomaly detection methods.
Authors:
; ;
Award ID(s):
1749940
Publication Date:
NSF-PAR ID:
10342093
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
36
Issue:
4
Page Range or eLocation-ID:
4101 to 4108
ISSN:
2159-5399
Sponsoring Org:
National Science Foundation
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