Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection

Timo Martens, Lorenzo Perini, Jesse Davis

Abstract: Anomaly detection aims at detecting examples that do not conform to normal behavior. Increasingly, anomaly detection is being approached from a semi-supervised perspective where active learning is employed to acquire a small number of strategically selected labels. However, because anomalies are not always well-understood events, the user may be uncertain about how to label certain instances. Thus, one can relax this request and allow the user to provide soft labels (i.e., probabilistic labels) that represent their belief that a queried example is anomalous. These labels are naturally noisy due to the user’s inherent uncertainty in the label and the fact that people are known to be bad at providing well-calibrated probability instances. To cope with these challenges, we propose to exploit a Gaussian Process to learn from actively acquired soft labels in the context of anomaly detection. This enables leveraging information about nearby examples to smooth out possible noise. Empirically, we compare our proposed approach to several baselines on 21 datasets and show that it outperforms them in the majority of experiments.