A new tabular anomaly detection model, PTAD, combines learnable masks in two spaces with optimal transport distances to learned prototypes and reports the best average AUC-PR and AUC-ROC on 20 benchmarks.
Random partitioning forest for point-wise and collective anomaly detection—application to network intrusion detection,
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Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
A new tabular anomaly detection model, PTAD, combines learnable masks in two spaces with optimal transport distances to learned prototypes and reports the best average AUC-PR and AUC-ROC on 20 benchmarks.