  This paper explores semi-supervised anomaly detection, a more practical
setting for anomaly detection where a small additional set of labeled samples
are provided. We propose a new KL-divergence based objective function for
semi-supervised anomaly detection, and show that two factors: the mutual
information between the data and latent representations, and the entropy of
latent representations, constitute an integral objective function for anomaly
detection. To resolve the contradiction in simultaneously optimizing the two
factors, we propose a novel encoder-decoder-encoder structure, with the first
encoder focusing on optimizing the mutual information and the second encoder
focusing on optimizing the entropy. The two encoders are enforced to share
similar encoding with a consistent constraint on their latent representations.
Extensive experiments have revealed that the proposed method significantly
outperforms several state-of-the-arts on multiple benchmark datasets, including
medical diagnosis and several classic anomaly detection benchmarks.
