Deep Nearest Neighbor Anomaly Detection

Liron Bergman; Niv Cohen; Yedid Hoshen

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arXiv · 2002.10445

Deep Nearest Neighbor Anomaly Detection

Liron Bergman·Niv Cohen·Yedid Hoshen

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Abstract

Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep methods (e.g. RotNet). Self-supervised features however typically under-perform Imagenet pre-trained features. In this work, we investigate whether the recent progress can indeed outperform nearest-neighbor methods operating on an Imagenet pretrained feature space. The simple nearest-neighbor based-approach is experimentally shown to outperform self-supervised methods in: accuracy, few shot generalization, training time and noise robustness while making fewer assumptions on image distributions.

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Liron Bergman, Niv Cohen, Yedid Hoshen. 2020-02-24. Deep Nearest Neighbor Anomaly Detection. https://arxiv.org/abs/2002.10445

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