Anomaly Detection via Few-Shot Learning on Normality

One of the basic ideas for anomaly detection is to describe an enclosing boundary of normal data in order to identify cases outside as anomalies. In practice, however, normal data can consist of multiple classes, in which case the anomalies may appear not only outside such an enclosure but also in-between 'normal' classes. This paper addresses deep anomaly detection aimed at embedding 'normal' classes to individually close but mutually distant proximities. We introduce a problem setting where a limited number of labeled examples from each 'normal' class is available for training. Preparing such examples is much more feasible in practice than collecting examples of anomalies or labeling large-scale, normal data. We utilize the labeled examples in a margin-based loss reflecting the inter-class and the intra-class distances among the embedded labeled data. The two terms and their relations are derived from an information-theoretic principle. In an empirical study using image benchmark datasets, we show the advantage of the proposed method over existing deep anomaly detection models. We also show case studies using low-dimensional mappings to analyze the behavior of the proposed method.