2 citations · 3 across the 2 of their papers we have counts for
4 papers
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…
Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment
Guy Amir, Ayala Prusak, Tal Reiss +2
Using multiple monitors is commonly thought to improve productivity, but this is hard to check experimentally. We use a survey, taken by 101 practitioners of which 80% have coded p…
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Tal Reiss, Niv Cohen, Liron Bergman +1
Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-superv…