An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination

TL;DR

Unsupervised anomaly detection (AD) methods typically assume clean training data, yet real-world datasets often contain undetected or mislabeled anomalies, leading to significant performance degradati…

Opening excerpt from the authors’ abstract. source