105 citations · 121 across the 7 of their papers we have counts for
3 papers · 1 filter
Reimagining Anomalies: What If Anomalies Were Normal?
Philipp Liznerski, Saurabh Varshneya, Ece Calikus +5
Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is pr…
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen +3
Due to the intractability of characterizing everything that looks unlike the normal data, anomaly detection (AD) is traditionally treated as an unsupervised problem utilizing only…
Explainable Deep One-Class Classification
Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen +3
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this tr…