Anomaly Detection Requires Better Representations

• URL: http://arxiv.org/abs/2210.10773v1
• Date: Wed, 19 Oct 2022 17:59:32 GMT
• Title: Anomaly Detection Requires Better Representations
• Authors: Tal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul, Yedid Hoshen
• Abstract summary: Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. Recent advances in self-supervised representation learning have driven improvements in anomaly detection.
• Score: 28.611440466398715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. We then argue that tackling the next generation of anomaly detection tasks requires new technical and conceptual improvements in representation learning.

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