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.
In the citing paper
“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.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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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]
The cited source says what the claim attributes to it.
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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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Suo, X., Zhang, J., Liu, J., Yang, D., & Zhou, F. (2023) · Sensors · doi:10.3390/s23156807 · Cited by 1
DRepT: Anomaly Detection Based on Transfer of Defect Representation with Transmittance Mask
Kobayashi, H., & Hashimoto, M. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191690 · Cited by 1
Wagenstetter, M., Aicher, T., Hellmich, A., & Ihlenfeldt, S. (2023) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin51400.2023.10218175 · Cited by 1
Implementing Multi-Level Features in a Student-Teacher Network for Anomaly Detection
Farady, I., Khimsuriya, B., Sagathiya, R., Lin, P.-C., & Lin, C. (2023) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan58799.2023.10226695 · Cited by 1
Defect detection of laptop appearance based on improved multi-scale normalizing flows
Zhang, J., Li, Z., & Zhao, Y. (2023) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac59482.2023.10401433 · Cited by 1
An adaptive incremental two-stage framework for crack defect detection
Guo, Q., Li, C., Deng, X., Dong, X., & Zhang, C. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18133-y · Cited by 1
Industrial defect detection and location based on greedy particle swarm optimization algorithm
Tang, Y., Yang, B., Peng, H. J., & Luo, X. (2024) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2024.2328530 · Cited by 1
Registration Based Feature Core-Set Model for Defect Detection
Jing, X. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450867 · Cited by 1
Industrial Image Anomaly Localization Method based on Reverse Knowledge Distillation
Zhang, T. (2024) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac59436.2024.10503620 · Cited by 1
Fast labeling pipeline approach for a huge aerial sensed dataset
Fedulin, A. M., & Voloshina, N. (2024) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2024-24-2-190-197 · Cited by 1
Unsupervised PCB Anomaly Segmentation with Foundational Models
Ho, C.-H., Seo, S.-B., Kim, N., Wu, P.-Y., Bae, Y., & Vasconcelos, N. M. (2024) · Electronic Imaging · doi:10.2352/ei.2024.36.6.iriacv-277 · Cited by 1
Huang, C., Blondheim, D., & Zhou, S. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02435-x · Cited by 1
Self-supervised Visual Anomaly Detection with Image Patch Generation and Comparison Networks
Huang, J., Zhao, K., Li, C., Lin, Y., Liu, Z., Wang, K., & Lian, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5609-4_8 · Cited by 1
Online Multivariate Time Series Anomaly Detection Method Based on Contrastive Learning
Dong, X., Liu, H., Du, J., Wang, Z., & Wang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5618-6_39 · Cited by 1
Multi-Layer Feature Restoration and Projection Model for Unsupervised Anomaly Detection
Cai, F., & Xia, S. (2024) · Mathematics · doi:10.3390/math12162480 · Cited by 1
Adjacent Image Augmentation and Its Framework for Self-Supervised Learning in Anomaly Detection
Kwon, G. S., & Choi, Y. S. (2024) · Sensors · doi:10.3390/s24175616 · Cited by 1
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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