  Although deep learning has been applied to successfully address many data
mining problems, relatively limited work has been done on deep learning for
anomaly detection. Existing deep anomaly detection methods, which focus on
learning new feature representations to enable downstream anomaly detection
methods, perform indirect optimization of anomaly scores, leading to
data-inefficient learning and suboptimal anomaly scoring. Also, they are
typically designed as unsupervised learning due to the lack of large-scale
labeled anomaly data. As a result, they are difficult to leverage prior
knowledge (e.g., a few labeled anomalies) when such information is available as
in many real-world anomaly detection applications.
  This paper introduces a novel anomaly detection framework and its
instantiation to address these problems. Instead of representation learning,
our method fulfills an end-to-end learning of anomaly scores by a neural
deviation learning, in which we leverage a few (e.g., multiple to dozens)
labeled anomalies and a prior probability to enforce statistically significant
deviations of the anomaly scores of anomalies from that of normal data objects
in the upper tail. Extensive results show that our method can be trained
substantially more data-efficiently and achieves significantly better anomaly
scoring than state-of-the-art competing methods.
