Efficient textile anomaly detection via memory guided distillation network
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02445-9.pdf
Reference52 articles.
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2. An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1), 1–18.
3. Bai, Z., & Jing, J. (2023). Mobile-deeplab: a lightweight pixel segmentation-based method for fabric defect detection. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-023-02205-1
4. Batzner, K., Heckler, L., & König, R. (2023). Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv:2303.14535
5. Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019). Mvtec ad—A comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, (pp. 9592–9600).
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