Semi-Supervised Anomaly Detection with Contrastive Regularization

Deep anomaly detection has recently seen significant developments to provide robust and efficient classifiers using only a few anomalous samples. Many of those models consist in a first isolated step of representation learning. However, in its current form the learned representation does not encode the semantics of normal sample and anomalies. Indeed during the first step these models will not utilize the available normal/anomaly labels, harming the downstream anomaly detection classifier performances. In the light of this limitation, we introduce a new deep anomaly detector enforcing an anomaly distance constraint on the norm of the representations while using contrastive learning on the direction of the features. This allows it to learn representations well-suited to anomaly detection while avoiding any representation collapse. Moreover, we introduce two strategies of anomaly enriching to improve the robustness of any distance-based anomaly detector. Our model highly improves the state-of-the-art performances on a wide array of anomaly types with up to 74% error relative improvement on object anomalies.