  An image anomaly localization method based on the successive subspace
learning (SSL) framework, called AnomalyHop, is proposed in this work.
AnomalyHop consists of three modules: 1) feature extraction via successive
subspace learning (SSL), 2) normality feature distributions modeling via
Gaussian models, and 3) anomaly map generation and fusion. Comparing with
state-of-the-art image anomaly localization methods based on deep neural
networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and
fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC)
performance on the MVTec AD dataset is 95.9%, which is among the best of
several benchmarking methods. Our codes are publicly available at Github.
