Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection

Bang Xiang Yong, Alexandra Brintrup

Abstract: 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.