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PaDiM: a patch distribution modeling framework for anomaly detection and localization

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-ofthe-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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Contributor : Angelique Loesch <>
Submitted on : Monday, June 7, 2021 - 12:35:55 PM
Last modification on : Wednesday, June 9, 2021 - 3:33:16 AM


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  • HAL Id : cea-03251821, version 1


Thomas Defard, Aleksandr Setkov, Angelique Loesch, Romaric Audigier. PaDiM: a patch distribution modeling framework for anomaly detection and localization. ICPR 2020 - 25th International Conference on Pattern Recognition Workshops and Challenges, Jan 2021, Milano, Italy. ⟨cea-03251821⟩



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