Anomalies, Representations, and Self-Supervision

no code implementations • 11 Jan 2023 • Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn

We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021.

Anomaly Detection Contrastive Learning