Supervised Anomaly Detection via Evidence-aware Reconstruction

Reconstruction-based anomaly detection methods have achieved strong performance by exploiting the discrepancy between reconstructed and observed patterns. However, reconstruction discrepancies are inherently ambiguous: large residuals may arise from difficult normal content, while anomalous regions can remain undetected when they are well reconstructed. This ambiguity leads to overlapping score distributions and limits reliable anomaly decisions under practical operating conditions. In this work, we propose Evidence-aware Reconstruction for Anomaly Detection (EviAD), a framework that uses a small set of annotated anomalies to augment reconstruction-based detectors with uncertainty-aware anomaly scoring. EviAD models two complementary forms of uncertainty: reconstruction uncertainty characterizes how difficult normal content is to reconstruct, helping reduce false alarms caused by large normal residuals; evidential uncertainty characterizes how strongly the learned evidence supports an anomaly prediction, helping identify anomalies that reconstruction errors alone may miss. Together, they guide anomaly scoring to better distinguish difficult normal samples from well-reconstructed anomalies. Extensive experiments demonstrate that EviAD consistently improves anomaly detection and localization performance, achieving higher recall under strict false-positive constraints and supporting more reliable industrial anomaly inspection.