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.
In the citing paper
“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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In the citing paper
“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]
The cited source says what the claim attributes to it.
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.
In the citing paper
“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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TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks
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Fu, Z., Pan, X., & Zhang, G. (2024) · IEEE Access · doi:10.1109/access.2024.3476469 · Cited by 2
Alzabidi, E., & Fındık, O. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-73420-5_5 · Cited by 1
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection
Zhang, Y., Cao, Y., Zhang, T., & Shen, W. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711818 · Cited by 1
Enhanced Anomaly Detection Using Spatial-Alignment and Multi-scale Fusion
Jiao, K., Yao, X., Wang, L., Zhang, B., Liu, Z., & Zhang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8493-6_21 · Cited by 1
Fractals as Pre-Training Datasets for Anomaly Detection and Localization
Ugwu, C. I., Caruso, E., & Lanz, O. (2024) · Fractal and Fractional · doi:10.3390/fractalfract8110661 · Cited by 1
Pantograph unsupervised anomaly detection network based on reverse distillation
Yan, H., Lin, C., Xu, Z., Guo, N., & Qing, A. (2024) · International Conference on Advances in Image Processing (ICAIP) · doi:10.1145/3702370.3702371 · Cited by 1
Geng, W., Zhu, F., Chen, S., Lv, Y., & Xiong, G. (2024) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi61353.2024.10778741 · Cited by 1
Roofus: Learning-based Robotic Moisture Mapping on Flat Rooftops with Ground Penetrating Radar
Lee, K., Lin, W.-H., Javed, T., Madhusudhan, S., Sher, B. A., & Feng, C. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802148 · Cited by 1
Evaluation of Fine-Grained Anomaly Detection Methods on a Novel Battery Surface Defect Dataset
Lin, D., Chen, Y., Tan, K., Zhou, Y., Xu, Y., & Zhai, Y. (2024) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1109/isceic63613.2024.10810185 · Cited by 1
Dual-flow feature enhancement network for robust anomaly detection in stainless steel pipe welding
Cao, R., Zhang, J., Yun, S., Zhou, H., Zhou, P., Shen, G., Xia, Z., Zang, Y., Liu, Q., & Hu, W. (2024) · The Visual Computer · doi:10.1007/s00371-024-03757-w · Cited by 2
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection
Cheung, T.-H., Fung, K.-C., Lai, S., Lin, K.-H., Ng, V., & Lam, K. (2024) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc63619.2025.10848778 · Cited by 2
Liu, Y., Zhang, Y., & Zhang, W. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865513 · Cited by 1
Long, J., Lan, X., Yan, S., Zhou, Y., Xiao, P., Xu, S., Wang, S., & Cai, N. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3545982 · Cited by 1
Point-plane better than point-point: for embedding-based USAD
Zhao, Z., Zhang, Y., Yu, Z., & Zhu, L. (2025) · International Conference on Video and Image Processing (ICVIP) · doi:10.1117/12.3059124 · Cited by 1
InstAD: Instance-aware Segmentation Framework for Zero-shot Multi-instance Anomaly Detection
Ho, C., & Lai, S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889356 · Cited by 2
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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