How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection

Felix Meissen, Johannes Getzner, Alexander Ziller +4

Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the…