  Recently, deep generative models have become increasingly popular in
unsupervised anomaly detection. However, deep generative models aim at
recovering the data distribution rather than detecting anomalies. Besides, deep
generative models have the risk of overfitting training samples, which has
disastrous effects on anomaly detection performance. To solve the above two
problems, we propose a Self-adversarial Variational Autoencoder with a Gaussian
anomaly prior assumption. We assume that both the anomalous and the normal
prior distribution are Gaussian and have overlaps in the latent space.
Therefore, a Gaussian transformer net T is trained to synthesize anomalous but
near-normal latent variables. Keeping the original training objective of
Variational Autoencoder, besides, the generator G tries to distinguish between
the normal latent variables and the anomalous ones synthesized by T, and the
encoder E is trained to discriminate whether the output of G is real. These new
objectives we added not only give both G and E the ability to discriminate but
also introduce additional regularization to prevent overfitting. Compared with
the SOTA baselines, the proposed model achieves significant improvements in
extensive experiments. Datasets and our model are available at a Github
repository.
