  We propose a supervised anomaly detection method based on neural density
estimators, where the negative log likelihood is used for the anomaly score.
Density estimators have been widely used for unsupervised anomaly detection. By
the recent advance of deep learning, the density estimation performance has
been greatly improved. However, the neural density estimators cannot exploit
anomaly label information, which would be valuable for improving the anomaly
detection performance. The proposed method effectively utilizes the anomaly
label information by training the neural density estimator so that the
likelihood of normal instances is maximized and the likelihood of anomalous
instances is lower than that of the normal instances. We employ an
autoregressive model for the neural density estimator, which enables us to
calculate the likelihood exactly. With the experiments using 16 datasets, we
demonstrate that the proposed method improves the anomaly detection performance
with a few labeled anomalous instances, and achieves better performance than
existing unsupervised and supervised anomaly detection methods.
