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Deep Nearest Neighbor Anomaly Detection

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arxiv 2002.10445 v1 pith:M3Z2OAPI submitted 2020-02-24 cs.LG cs.CVstat.ML

Deep Nearest Neighbor Anomaly Detection

Liron Bergman , Niv Cohen , Yedid Hoshen This is my paper
classification cs.LG cs.CVstat.ML
keywords methodsself-supervisedanomalydeepdetectionfeaturesimagenetnearest
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 5 Pith papers

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