  Deep approaches to anomaly detection have recently shown promising results
over shallow methods on large and complex datasets. Typically anomaly detection
is treated as an unsupervised learning problem. In practice however, one may
have---in addition to a large set of unlabeled samples---access to a small pool
of labeled samples, e.g. a subset verified by some domain expert as being
normal or anomalous. Semi-supervised approaches to anomaly detection aim to
utilize such labeled samples, but most proposed methods are limited to merely
including labeled normal samples. Only a few methods take advantage of labeled
anomalies, with existing deep approaches being domain-specific. In this work we
present Deep SAD, an end-to-end deep methodology for general semi-supervised
anomaly detection. We further introduce an information-theoretic framework for
deep anomaly detection based on the idea that the entropy of the latent
distribution for normal data should be lower than the entropy of the anomalous
distribution, which can serve as a theoretical interpretation for our method.
In extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with
other anomaly detection benchmark datasets, we demonstrate that our method is
on par or outperforms shallow, hybrid, and deep competitors, yielding
appreciable performance improvements even when provided with only little
labeled data.
