Semi-Supervised Anomaly Detection with Contrastive Regularization
Résumé
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