Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise
Open accessAbstract
In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. We show that this model favors the reconstruction of clean images from arbitrary real-world images, regardless of the actual defects appearance. In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset [1], both for pixel- and image-wise anomaly detection. Our implementation is available at https://github.com/anncollin/AnomalyDetection-Keras.
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Moon, A.-S., Lee, K., & Lee, J. (2025) · IEEE Access · doi:10.1109/access.2025.3585843 · Cited by 2 · see citing works
DualAD: Exploring Coupled Dual-Branch Networks for Multi-Class Unsupervised Anomaly Detection
He, S., Chen, Y., Wang, L., Huang, W., Xu, R., & Qian, Y. (2025) · Electronics · doi:10.3390/electronics14030594 · Cited by 2 · see citing works
Robust Anomaly Detection and Localization via Simulated Anomalies
Chen, Y., Wang, M., Wang, D., & Li, D. (2022) · ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI) · doi:10.1145/3574131.3574463 · Cited by 2 · see citing works
Collin, A.-S., de Bodt, C., Mulders, D., & Vleeschouwer, C. D. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-139 · Cited by 2 · see citing works
Self-citationPGTNet: Prototype Guided Transfer Network for Few-Shot Anomaly Localization
Zhuang, Z., Zhang, J., Xiao, L., Ma, T., & He, L. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897566 · Cited by 1 · see citing works
Yang, Q., Huang, B., Zhou, Q., Wang, Z., Chen, K., Xiu, T., Yao, C., & Wu, S. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3633279 · Cited by 1 · see citing works
A Lightweight and Robust Low-Rank Tensor Embedding-Integrated VAE for Network Anomaly Detection
Liu, M., Lin, M., Yu, S., Chen, R., & Luo, X. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3716561 · Cited by 1 · see citing works
Subspace-Based Embedded Feature Reduction for Fast Anomaly Detection
Murakami, N., Hiramatsu, N., Kobayashi, H., Akizuki, S., & Hashimoto, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-95918-9_15 · Cited by 1 · see citing works
Li, G., Zou, Y., Liu, B., Jiang, N., Lv, J., Ai, W., Ji, D., Zhou, Q., & Zhang, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131345 · Cited by 1 · see citing works
Unsupervised Retinal Lesion Detection by Learning to Restore Corrupted Fundus Images
Liu, H., Du, Y., An, C., & Wang, L. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761586 · Cited by 1 · see citing works
BPGG: Bidirectional Prototype Generation and Guidance Network for Few-Shot Anomaly Localization
Zhang, J., Zhuang, Z., Xu, J., Ma, T., & He, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15934-3_3 · Cited by 1 · see citing works
Pseudo-Supervised Defect Detection Using Robust Deep Convolutional Autoencoders
Alpdemir, M. N. (2022) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1196381 · Cited by 1 · see citing works
Image Anomaly Detection With Semantic- Enhanced Augmentation and Distributional Kernel
Wang, M., Zhu, Y., Li, G., Liu, G., & Yang, B. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00054 · Cited by 1 · see citing works
Suo, X., Zhang, J., Liu, J., Yang, D., & Zhou, F. (2023) · Sensors · doi:10.3390/s23156807 · Cited by 1 · see citing works
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 · see citing works
Enhanced Reverse Distillation Guided Segmentation Network for Anomaly Detection
Li, X., Ma, X., Chen, J., Xu, Q., & Yang, L. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10649917 · Cited by 1 · see citing works
Unsupervised Variability Normalization For Anomaly Detection
Artola, A., Kolodziej, Y., Morel, J., & Ehret, T. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506742 · Cited by 1 · see citing works
Factorized Industrial Anomaly Detection and Localization
Zhu, Y., Dai, L., Dong, X., & Li, P. (2023) · IEEE Access · doi:10.1109/access.2023.3265715 · Cited by 1 · see citing works
Joint Use of Skip Connections and Synthetic Corruption for Anomaly Detection with Autoencoders
Collin, A.-S., & Vleeschouwer, C. D. (2021) · Springer series in reliability engineering · doi:10.1007/978-3-030-83819-5_8
Self-citationTong, Q., Sun, M., Wang, B., & Liu, D. (2021) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3498851.3498981
A Novel Contrastive Learning Framework for Self-Supervised Anomaly Detection
Li, J., Lian, Z., & Li, M. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9898024
Multiresolution Knowledge Distillation and Multi-level Fusion for Defect Detection
Xie, H., & Xiao, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26118-3_14
Image Reconstruction and Defect Detection Method Based on Broad Learning System
郑, 泽. (2023) · Computer Science and Application · doi:10.12677/csa.2023.133057
Connective Reconstruction-Based Novelty Detection
Hashemi, S. M., Aliniya, P., & Razzaghi, P. (2023) · doi:10.1109/icee59167.2023.10334839
A proposal of anomaly detection method based on natural data augmentation in the Eigenspace
Murakami, N., Hiramatsu, N., Kobayashi, H., Akizuki, S., & Hashimoto, M. (2024) · Journal of WSCG · doi:10.24132/jwscg.2024.10
High-Reality Defect Image Generation Based on Fusion of Information in Eigen Space
Murakami, N., HIRAMATSU, N., Kobayashi, H., Akizuki, S., & Hashimoto, M. (2024) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.90.662
Köse, H., & KILIÇ, S. Ş. (2024) · Güncel Pediatri · doi:10.4274/jcp.2024.81489
Jha, R. R., Teja, A. S. S., Wadawadagi, V., & Tallamraju, R. B. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651095
HIRAMATSU, N., Kobayashi, H., Akizuki, S., & Hashimoto, M. (2025) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.91.185
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
Park, Y., Kang, S., Kim, M. J., Kim, H. S., & Yi, J. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00377
Xu, H., & Yang, B. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3659501
- Authors
- Collin, A.-S., & De Vleeschouwer, C.
- Affiliations
- UCLouvain (BE)
- ORCID
- Collin, Anne-Sophie
- Venue
- 2020 25th International Conference on Pattern Recognition (ICPR)
- Publisher
- IEEE
- Published
- 2021-01-10
- Pages
- 7915-7922
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W3082804066
- Cited by
- 106
- References
- 27
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 33 · 2023: 17 · 2022: 18 · 2021: 8
- FWCI
- 26.09
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- anomaly detection, autoencoder, skip connections, industrial vision, image reconstruction, reconstruction residuals, reconstruction uncertainty, MVTec AD
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