  The goal of anomaly detection is to identify anomalous samples from normal
ones. In this paper, a small number of anomalies are assumed to be available at
the training stage, but they are assumed to be collected only from several
anomaly types, leaving the majority of anomaly types not represented in the
collected anomaly dataset at all. To effectively leverage this kind of
incomplete anomalous knowledge represented by the collected anomalies, we
propose to learn a probability distribution that can not only model the normal
samples, but also guarantee to assign low density values for the collected
anomalies. To this end, an anomaly-aware generative adversarial network (GAN)
is developed, which, in addition to modeling the normal samples as most GANs
do, is able to explicitly avoid assigning probabilities for collected anomalous
samples. Moreover, to facilitate the computation of anomaly detection criteria
like reconstruction error, the proposed anomaly-aware GAN is designed to be
bidirectional, attaching an encoder for the generator. Extensive experimental
results demonstrate that our proposed method is able to effectively make use of
the incomplete anomalous information, leading to significant performance gains
compared to existing methods.
