PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

• URL: http://arxiv.org/abs/2011.08785v1
• Date: Tue, 17 Nov 2020 17:29:18 GMT
• Title: PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
• Authors: Thomas Defard, Aleksandr Setkov, Angelique Loesch, Romaric Audigier
• Abstract summary: We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding. It also exploits correlations between the different semantic levels of CNN to better localize anomalies.
• Score: 64.39761523935613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-world visual industrial inspection, we extend the evaluation protocol to assess performance of anomaly localization algorithms on non-aligned dataset. The state-of-the-art performance and low complexity of PaDiM make it a good candidate for many industrial applications.

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