Gastaldi, X. Shake-shake regularization. arXiv preprint arXiv:1705.07485, 2017.
Goodfellow, I. J., Shlens, J., and Szegedy, C. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.
Gowal, S., Qin, C., Uesato, J., Mann, T., and Kohli, P. Uncovering the limits of adversarial training against norm-bounded adversarial examples. arXiv preprint arXiv:2010.03593, 2020.
Hassibi, B. and Stork, D. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems, 5, 1992.
Hendrycks, D., Lee, K., and Mazeika, M. Using pre-training can improve model robustness and uncertainty. In International Conference on Machine Learning, pp. 2712-2721. PMLR, 2019a.
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B. Aug-mix: A simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:1912.02781, 2019b.
Ioffe, S. and Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448-456. PMLR, 2015.
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407, 2018.
Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International conference on machine learning, pp. 2304-2313. PMLR, 2018.
Jiang, L., Huang, D., Liu, M., and Yang, W. Beyond synthetic noise: Deep learning on controlled noisy labels. In International Conference on Machine Learning, pp. 4804-4815. PMLR, 2020.
Kannan, H., Kurakin, A., and Goodfellow, I. Adversarial logit pairing. arXiv preprint arXiv:1803.06373, 2018.
Krizhevsky, A., Hinton, G., et al. Learning multiple layers of features from tiny images. 2009.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84-90, 2017.
Krogh, A. and Hertz, J. A simple weight decay can improve generalization. Advances in neural information processing systems, 4, 1991.
Kuriyama, K. Autocleansing: Unbiased estimation of deep learning with mislabeled data. 2020.
Kwon, J., Kim, J., Park, H., and Choi, I. K. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In International Conference on Machine Learning, pp. 5905-5914. PMLR, 2021.
LeCun, Y. and Cortes, C. MNIST handwritten digit database. 2010. URL http://yann.lecun.com/exdb/mnist/
LeCun, Y., Denker, J., and Solla, S. Optimal brain damage. Advances in neural information processing systems, 2, 1989.
Liu, F., Han, B., Liu, T., Gong, C., Niu, G., Zhou, M., Sugiyama, M., et al. Probabilistic margins for instance reweighting in adversarial training. Advances in Neural Information Processing Systems, 34:23258-23269, 2021.
Loshchilov, I. and Hutter, F. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083, 2017.