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.
“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]
“Learning of the normality” (p. 3) says PaDiM estimates a covariance matrix for each patch and makes it invertible. “Ablative studies” (pp. 4–5) says larger embedding vectors increase computational and memory complexity. The copy does not say that PaDiM requires a batch-inverse operation or that this makes scaling to larger CNNs challenging; “Scalability gain” (p. 6) instead describes choosing the backbone and embedding size to fit resource limits. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPatchCore, SPADE, and PaDim conduct an ablation study on 16 normal training samples.
“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]
The 'Ablative studies' section (pp. 4–5) reports ablations of PaDiM’s semantic-level correlations and dimensionality reduction. It does not report an ablation using 16 normal training samples. The paper evaluates SPADE as a comparator but does not establish that SPADE or PatchCore conducted the claimed 16-sample ablation. (LLM, conf=0.84)
Experimental — accuracy not yet measuredExisting unsupervised anomaly detection methods train separate models for different object classes.
“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]
The Experimental setups section (p. 4) says the paper's VAE baseline is trained on each MVTec AD class, supporting class-specific training as an example. But the Scalability gain section (p. 6) says SPADE requires no training, so the paper does not support the broader claim that existing methods generally require separate models for different objects. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFeature-based approaches commonly employ networks pre-trained on ImageNet to map original images into a more distinguishable feature space, with low training costs and outstanding performance.
“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]
Aggregation of multiple feature hierarchies offers some benefit.
“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]
PatchCore uses locally aggregated, mid-level feature patches.
“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]
More global context can be achieved by moving down the network hierarchy, but at the cost of reduced resolution and heavier ImageNet class bias.
“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]
For PaDiM, the memory bank size is determined solely by the image resolution and not by the size of the training set.
“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]
PaDiM employs multivariate Gaussian distributions to construct a probabilistic representation of the normal class.
“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]
With coreset subsampling, PatchCore has lower inference times than PaDiM.
“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]
The Scalability gain section (p. 6, Table VII) reports PaDiM inference times, but it does not compare them with PatchCore. Searches for PatchCore and coreset subsampling found no passage addressing whether subsampling makes PatchCore faster than PaDiM. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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- 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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