Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators

Understand

Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance).

• Deep learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other errors.
• We show that this approach exhibits intrinsic biases that lead to undesirable results.
• Reconstruction-based methods are sensitive to training-data outliers and simple-to-reconstruct points.