Anomaly Classification

Anomaly Classification is the task of identifying and categorizing different types of anomalies in visual data, rather than simply detecting whether an input is normal or anomalous. Unlike anomaly detection, which is typically a binary classification (normal vs. anomaly), anomaly classification requires distinguishing between multiple anomaly classes—each representing a distinct type of anomaly or irregularity. This task is critical in real-world applications such as industrial inspection, where different anomalies may require different responses or interventions.