Robust Anomaly Detection in Images using Adversarial Autoencoders

Understand

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis.

• Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalies, where the reconstruction error exceeds some threshold.
• Here we analyze a fundamental problem of this approach when the training set is contaminated with a small fraction of outliers.
• We find that continued training of autoencoders inevitably reduces the reconstruction error of outliers, and hence degrades the anomaly detection performance.