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Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance.
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2018
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2018
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2018
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P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, “Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9592–9600
2019
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E. Jardim, L. A. Thomaz, E. A. da Silva, and S. L. Netto, “Domain-transformable sparse representation for anomaly detection in moving-camera videos,” IEEE Transactions on Image Processing , vol. 29, pp. 1329–1343, 2019
2019
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D. Tabernik, S. Šela, J. Skvarč, and D. Skočaj, “Segmentation-Based Deep-Learning Approach for Surface-Defect Detection,” Journal of Intelligent Manufacturing , May 2019
2019
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2019
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M. Z. Zaheer, J.-h. Lee, M. Astrid, and S.-I. Lee, “Old is gold: Redefining the adversarially learned one-class classifier training paradigm,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 183–14 193
2020
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2020
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2020
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2020
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2021
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2021
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2021
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C.-L. Li, K. Sohn, J. Yoon, and T. Pfister, “Cutpaste: Self-supervised learning for anomaly detection and localization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9664–9674
2021
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2021
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V. Zavrtanik, M. Kristan, and D. Skočaj, “Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 8330–8339
2021
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O. Rippel, P. Mertens, and D. Merhof, “Modeling the distribution of normal data in pre-trained deep features for anomaly detection,” in 2020 25th International Conference on Pattern Recognition (ICPR) . IEEE, 2021, pp. 6726–6733
2021
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Z. Zeng, B. Liu, J. Fu, and H. Chao, “Reference-based defect detection network,” IEEE Transactions on Image Processing , vol. 30, pp. 6637–6647, 2021
2021
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2021
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K. Jiang, W. Xie, J. Lei, T. Jiang, and Y. Li, “Lren: Low-rank embedded network for sample-free hyperspectral anomaly detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 5, 2021, pp. 4139–4146
2021
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X. Han, X. Chen, and L.-P. Liu, “Gan ensemble for anomaly detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 5, 2021, pp. 4090–4097
2021
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2021
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K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, and P. Gehler, “Towards total recall in industrial anomaly detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 14 318–14 328
2022
Closest in time.
J. Pirnay and K. Chai, “Inpainting transformer for anomaly detection,” in Image Analysis and Processing–ICIAP 2022: 21st International Conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part II . Springer, 2022, pp. 394–406
2022
Closest in time.