Sub-Image Anomaly Detection with Deep Pyramid Correspondences

5 May 2020  ·  Niv Cohen, Yedid Hoshen ·

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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Results from the Paper

Ranked #58 on Anomaly Detection on MVTec AD (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Anomaly Detection MVTec AD SPADE Detection AUROC 85.5 # 58
Segmentation AUROC 96.5 # 34


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