Revisiting segmentation-guided denoising student–teacher in anomaly detection
Author:
Funder
the Public Welfare Research Program of Huzhou Science and Technology Bureau
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03412-4.pdf
Reference53 articles.
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2. Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357 (2020)
3. Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: International Conference on Pattern Recognition, pp. 475–489. Springer (2021). https://doi.org/10.1007/978-3-030-68799-1_35
4. Zavrtanik, V., Kristan, M., Skočaj, D.: Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8330–8339 (2021). https://doi.org/10.1109/iccv48922.2021.00822
5. Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: European Conference on Computer Vision, pp. 392–408 (2022). Springer. https://doi.org/10.1007/978-3-031-20056-4_23
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