Anomalous Samples for Few-Shot Anomaly Detection

Several anomaly detection and classification methods rely on large amounts of non-anomalous or “normal” samples under the assumption that anomalous data is typically harder to acquire. This hypothesis becomes questionable in Few-Shot settings, where as little as one annotated sample can make a significant difference. In this paper, we tackle the question of utilizing anomalous samples in training a model for binary anomaly classification. We propose a methodology that incorporates anomalous samples in a multi-score anomaly detection score leveraging recent Zero-Shot and memory-based techniques. We compare the utility of anomalous samples to that of regular samples and study the benefits and limitations of each. In addition, we propose an augmentation-based validation technique to optimize the aggregation of the different anomaly scores and demonstrate its effectiveness on popular industrial anomaly detection datasets.