Anomaly Severity Classification (Anomaly vs. Defect)

Anomaly Severity Classification is a novel task that extends traditional anomaly detection by distinguishing between negligible anomalies and critical defects. While existing benchmarks focus on identifying whether an input deviates from normality, this task introduces a deployment-oriented perspective—evaluating the actionability of detected anomalies. For example, cosmetic deviations or design shifts may be acceptable (anomalies), while structural damages require intervention (defects). This task supports more informed and automated decision-making in industrial and quality control settings. It was introduced in the VELM paper, alongside a simulation benchmark derived from the MVTec-AC dataset.