Anomaly Detection Requires Better Representations

Understand

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 training.
• Recent advances in self-supervised representation learning have directly driven improvements in anomaly detection.
• In this position paper, we first explain how self-supervised representations can be easily used to achieve state-of-the-art performance in commonly reported anomaly detection benchmarks.