Tal Reiss

Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment

2021

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 professionally for at least 2 years, …

Mean-Shifted Contrastive Loss for Anomaly Detection

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dataset sizes limit its effectiveness. It was previously shown that utilizing external, generic datasets …

No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection

2023 · arXiv (Cornell University)

Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anomaly detection can improve indefinitely by increasing the scale of our …