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Sub-Image Anomaly Detection with Deep Pyramid Correspondences

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arxiv 2005.02357 v3 pith:B5O7VR3N submitted 2020-05-05 cs.CV cs.LG

Niv Cohen , Yedid Hoshen This is my paper
classification cs.CVcs.LG
keywords anomalydetectionpyramidcorrespondencesdeepimageimagesmethods
verification ladder T0 review T1 audit T2 compute T3 formal
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Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on unsupervised anomaly detection and localization while requiring virtually no training time.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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