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arXiv · 2210.10773

Anomaly Detection Requires Better Representations

Tal Reiss·Niv Cohen·Eliahu Horwitz·Ron Abutbul·Yedid Hoshen
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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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Tal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul, Yedid Hoshen. 2022-10-19. Anomaly Detection Requires Better Representations. https://arxiv.org/abs/2210.10773

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