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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 Connect in order to contact the contributor
Submitted on : Monday, June 7, 2021 - 12:35:55 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Wednesday, September 8, 2021 - 6:45:06 PM


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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. pp.475-489, ⟨10.1007/978-3-030-68799-1_35⟩. ⟨cea-03251821⟩



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