Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
Open accessAbstract
The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD [4] and Magnetic Tile Defects [14] datasets.
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DifferNet [52] is the first research to use NF to address the industrial image AD issue.
“DifferNet [52] is the first research to use NF to address the industrial image AD issue.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [52]
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“Other unsupervised representation learning methods can similarly be utilised, such as via GANs [13], learning to predict predefined geometric transformations [20] or via normalizing flows [42].”
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Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
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- Authors
- Rudolph, M., Wandt, B., & Rosenhahn, B.
- Affiliations
- Leibniz University Hannover (DE)
- Venue
- 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2021-01
- Pages
- 1906-1915
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W3081828334
- Cited by
- 489
- References
- 2
- Citations by year
- 2026: 59 · 2025: 125 · 2024: 139 · 2023: 94 · 2022: 54 · 2021: 17 · 2020: 1
- FWCI
- 104.42
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- defect detection, semi-supervised learning, normalizing flows, manufacturing quality inspection, multi-scale feature extraction, anomaly detection, low-resource training, MVTec AD
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