Adversarial Anomaly Detection using Gaussian Priors and Nonlinear
  Anomaly Scores
Fiete Lüer Tobias Weber Maxim Dolgich Christian Böhm
Anomaly detection in imbalanced datasets is a frequent and crucial problem,
especially in the medical domain where retrieving and labeling irregularities
is often expensive. By combining the generative stability of a
$\beta$-variational autoencoder (VAE) with the discriminative strengths of
generative adversarial networks (GANs), we propose a novel model,
$\beta$-VAEGAN. We investigate methods for composing anomaly scores based on
the discriminative and reconstructive capabilities of our model. Existing work
focuses on linear combinations of these components to determine if data is
anomalous. We advance existing work by training a kernelized support vector
machine (SVM) on the respective error components to also consider nonlinear
relationships. This improves anomaly detection performance, while allowing
faster optimization. Lastly, we use the deviations from the Gaussian prior of
$\beta$-VAEGAN to form a novel anomaly score component. In comparison to
state-of-the-art work, we improve the $F_1$ score during anomaly detection from
0.85 to 0.92 on the widely used MITBIH Arrhythmia Database.