327 citations · 466 across the 16 of their papers we have counts for
5 papers · 1 filter
Adapting prior-data fitted networks for tabular anomaly detection
Maximilian Bershtman, Niv Cohen
While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the limited availability of strong…
Classifying Nodes in Graphs without GNNs
Daniel Winter, Niv Cohen, Yedid Hoshen
Graph neural networks (GNNs) are the dominant paradigm for classifying nodes in a graph, but they have several undesirable attributes stemming from their message passing architectu…
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
Tal Reiss, Niv Cohen, Yedid Hoshen
Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anom…
Anomaly Detection Requires Better Representations
Tal Reiss, Niv Cohen, Eliahu Horwitz +2
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during tr…
Deep Nearest Neighbor Anomaly Detection
Liron Bergman, Niv Cohen, Yedid Hoshen
Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep methods (e.g. RotNet).…