@article{
 title = {PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization},
 type = {article},
 year = {2020},
 keywords = {Anomaly detection,Anomaly localization,Computer vision},
 pages = {475-489},
 volume = {12664 LNCS},
 websites = {https://arxiv.org/abs/2011.08785v1},
 month = {11},
 publisher = {Springer Science and Business Media Deutschland GmbH},
 day = {17},
 id = {ed356bdd-3e4e-304e-988a-ea243c2f7792},
 created = {2023-06-12T12:08:53.961Z},
 accessed = {2023-06-12},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2023-06-13T05:14:56.486Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {5a010301-acb6-4642-a6b2-8afaee1b741c,4b11b59b-846d-45b5-82b7-81d8bc3108b2,bd3c6f2e-3514-47cf-bc42-12db8b9abe45},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Defard, Thomas and Setkov, Aleksandr and Loesch, Angelique and Audigier, Romaric},
 doi = {10.1007/978-3-030-68799-1_35},
 journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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PaDiM makes use of a pretrained convolutional neural network (CNN) for\npatch embedding, and of multivariate Gaussian distributions to get a\nprobabilistic representation of the normal class. It also exploits correlations\nbetween the different semantic levels of CNN to better localize anomalies.\nPaDiM outperforms current state-of-the-art approaches for both anomaly\ndetection and localization on the MVTec AD and STC datasets. To match\nreal-world visual industrial inspection, we extend the evaluation protocol to\nassess performance of anomaly localization algorithms on non-aligned dataset.\nThe state-of-the-art performance and low complexity of PaDiM make it a good\ncandidate for many industrial applications.","bibtype":"article","author":"Defard, Thomas and Setkov, Aleksandr and Loesch, Angelique and Audigier, Romaric","doi":"10.1007/978-3-030-68799-1_35","journal":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","bibtex":"@article{\n title = {PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization},\n type = {article},\n year = {2020},\n keywords = {Anomaly detection,Anomaly localization,Computer vision},\n pages = {475-489},\n volume = {12664 LNCS},\n websites = {https://arxiv.org/abs/2011.08785v1},\n month = {11},\n publisher = {Springer Science and Business Media Deutschland GmbH},\n day = {17},\n id = {ed356bdd-3e4e-304e-988a-ea243c2f7792},\n created = {2023-06-12T12:08:53.961Z},\n accessed = {2023-06-12},\n file_attached = {true},\n profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},\n group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},\n last_modified = {2023-06-13T05:14:56.486Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {5a010301-acb6-4642-a6b2-8afaee1b741c,4b11b59b-846d-45b5-82b7-81d8bc3108b2,bd3c6f2e-3514-47cf-bc42-12db8b9abe45},\n private_publication = {false},\n abstract = {We present a new framework for Patch Distribution Modeling, PaDiM, to\nconcurrently detect and localize anomalies in images in a one-class learning\nsetting. 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