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We study anomaly clustering, grouping data into coherent clusters of anomalytypes. This is different from anomaly detection that aims to divide anomaliesfrom normal data. Unlike object-centered image clustering, anomaly clusteringis particularly challenging as anomalous patterns are subtle and local. Wepresent a simple yet effective clustering framework using a patch-basedpretrained deep embeddings and off-the-shelf clustering methods. We define adistance function between images, each of which is represented as a bag ofembeddings, by the Euclidean distance between weighted averaged embeddings. Theweight defines the importance of instances (i.e., patch embeddings) in the bag,which may highlight defective regions. We compute weights in an unsupervisedway or in a semi-supervised way when labeled normal data is available.Extensive experimental studies show the effectiveness of the proposedclustering framework along with a novel distance function upon exist-ingmultiple instance or deep clustering frameworks. Over-all, our frameworkachieves 0.451 and 0.674 normalized mutual information scores on MVTec objectand texture categories and further improve with a few labeled normal data(0.577, 0.669), far exceeding the baselines (0.244, 0.273) or state-of-the-artdeep clustering methods (0.176, 0.277).

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types