Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores
This work addresses the problem of detecting anomalies in expensive-to-label medical data, representing an incremental advance over existing methods.
The paper tackled anomaly detection in imbalanced datasets, such as in medical domains, by proposing a novel model called β-VAEGAN that combines a β-VAE with GANs and uses a kernelized SVM for nonlinear anomaly scoring, improving the F1 score from 0.85 to 0.92 on the MITBIH Arrhythmia Database.
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 $β$-variational autoencoder (VAE) with the discriminative strengths of generative adversarial networks (GANs), we propose a novel model, $β$-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 $β$-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.