The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization
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“PaDiM requires the batch-inverse of the multidimensional covariance tensor, which makes it challenging to scale up to larger CNNs due to the increased feature size.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“PatchCore [68] , SPADE [63] and PaDim [73] conduct the ablation study on 16 normal training samples.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [11]
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“Feature-based approaches [17,18,19] commonly employ networks pre-trained on ImageNet [20] to map original images into a more distinguishable feature space. These methods are typically characterized by low training costs and outstanding performance.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [19]
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“Empirically and similar to [10] and [14], we found aggregation of multiple feature hierarchies to offer some benefit.”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
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“Relying on the fact that an image can be already classified as anomalous as soon as a single patch is anomalous [14, 56], PatchCore achieves this by utilizing locally aggregated, mid-level features patches.”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
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“More global context can also be achieved by moving down the network hierarchy (see e.g. [10, 14]), however at the cost of reduced resolution and heavier ImageNet class bias (§3.1).”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
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In the citing paper
“Consequently, the memory bank size is determined solely by the image resolution and not by the size of the training set.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
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“PaDim [73] employs multivariate Gaussian distributions to construct a probabilistic representation of the normal class.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
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In the citing paper
“With coreset subsampling, Patchcore can be made even faster, with lower inference times than even PaDiM while retaining state-of-the-art image-level anomaly detection and segmentation performance.”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
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Citations
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Metadata
- Authors
- Defard, T., Setkov, A., Loesch, A., & Audigier, R.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Setkov, Aleksandr
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Volume
- 12664
- Pages
- 475-489
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3099658315
- Cited by
- 1,415
- References
- 32
- Citations by year
- 2026: 325 · 2025: 391 · 2024: 367 · 2023: 217 · 2022: 88 · 2021: 27
- FWCI
- 80.73
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- anomaly detection, anomaly localization, one-class learning, multivariate normal distribution, patch embedding, pre-trained convolutional neural networks, MVTec AD
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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