  We propose the Autoencoding Binary Classifiers (ABC), a novel supervised
anomaly detector based on the Autoencoder (AE). There are two main approaches
in anomaly detection: supervised and unsupervised. The supervised approach
accurately detects the known anomalies included in training data, but it cannot
detect the unknown anomalies. Meanwhile, the unsupervised approach can detect
both known and unknown anomalies that are located away from normal data points.
However, it does not detect known anomalies as accurately as the supervised
approach. Furthermore, even if we have labeled normal data points and
anomalies, the unsupervised approach cannot utilize these labels. The ABC is a
probabilistic binary classifier that effectively exploits the label
information, where normal data points are modeled using the AE as a component.
By maximizing the likelihood, the AE in the proposed ABC is trained to minimize
the reconstruction error for normal data points, and to maximize it for known
anomalies. Since our approach becomes able to reconstruct the normal data
points accurately and fails to reconstruct the known and unknown anomalies, it
can accurately discriminate both known and unknown anomalies from normal data
points. Experimental results show that the ABC achieves higher detection
performance than existing supervised and unsupervised methods.
