1. (1) O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei: “ImageNet Large Scale Visual Recognition Challenge”, International Journal of Computer Vision (2015)
2. (2) T. Defard, A. Setkov, A. Loesch, and R. Audigier: “Padim: A patch distribution modeling framework for anomaly detection and localization”, In International Conference on Machine Learning, pp. 475-489 (2021)
3. (3) 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 (CVPR) (2019)
4. (4) K. He, X. Zhang, S. Ren, and J. Sun: “Deep residual learning for image recognition”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778 (2016)
5. (5) R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel: “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness”, In 7th International Conference on Learning Representations (2019)