  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.
