Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection

作者：

Highlights：

• Formulation of Bayesian autoencoders is extended to quantify anomaly uncertainty.

• The total anomaly uncertainty comprises epistemic and aleatoric uncertainties.

• Rejection of predictions with high uncertainty improves performance.

• Validation of proposed methods on multiple benchmarks and real use cases.

摘要

•Formulation of Bayesian autoencoders is extended to quantify anomaly uncertainty.•The total anomaly uncertainty comprises epistemic and aleatoric uncertainties.•Rejection of predictions with high uncertainty improves performance.•Validation of proposed methods on multiple benchmarks and real use cases.

论文关键词：Bayesian autoencoders,Anomaly detection,Uncertainty,Trustworthy machine learning

论文评审过程：Received 25 January 2022, Revised 22 June 2022, Accepted 15 July 2022, Available online 26 July 2022, Version of Record 6 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118196