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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]
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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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Real-Time Industrial Anomaly Detection via Sparse Reconstruction
Zhou, C., Wan, J., & Zhao, X. (2024) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar61438.2024.10670980
High-Speed Rail Infrastructure Defect Image Detection Based on Pretrained Unsupervised Models
Chang, L. T., Yu, C., Li, S., & Zhao, X. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10662445
Siamese Contrastive Reverse Distillation for Industrial Anomaly Localization
Liang, D., Du, Y., & Tan, S. (2024) · International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) · doi:10.1145/3653644.3653649
Apnet: Generating Precise Anomaly Prior Information for Mixed-Supervised Defect Detection
Li, G., & Gao, H. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647680
ATAC-NET: Zoomed View Works Better for Anomaly Detection
Gupta, S., Gautam, N., & Malyala, A. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647702 · Cited by 1
Towards training noise-robust anomaly detection via collaborative adversarial flows
Cheng, H., Luo, J., Zhang, X., Liu, H., & Wu, F. (2024) · Measurement · doi:10.1016/j.measurement.2024.115865
Global-Local Consistency for Cost-Effective Anomaly Localization with Point Annotations
Nakanishi, K., Katafuchi, R., & Tokunaga, T. (2024) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai63651.2024.00019
Local-GAN: An Anomaly Detection Method Based on Local Key Features and GAN for IoT
Lai, H., Zhou, F., Wang, B., Chen, H., & Zhang, S. (2024) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00042
Interpretable Unsupervised Homography Estimation
Zhou, Z., Zhu, Q. X., Wang, Y., Mo, Y., Chen, L., Huang, J., & Jiang, T. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8490-5_28
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
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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.
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