Al-Rawi, M. and Karatzas, D. On the labeling correctness in computer vision datasets. In IAL@ PKDD/ECML, pp. 1-23, 2018.
Andriushchenko, M., Croce, F., Flammarion, N., and Hein, M. Square attack: a query-efficient black-box adversarial attack via random search. In European Conference on Computer Vision, pp. 484-501. Springer, 2020.
Balaji, Y., Goldstein, T., and Hoffman, J. Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets. arXiv preprint arXiv:1910.08051, 2019.
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D. Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473):138-156, 2006.
Bergstra, J., Yamins, D., and Cox, D. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning, pp. 115-123. PMLR, 2013.
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F. Evasion attacks against machine learning at test time. In Joint European conference on machine learning and knowledge discovery in databases, pp. 387-402. Springer, 2013.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
Carlini, N. and Wagner, D. Towards evaluating the robustness of neural networks. In 2017 IEEE symposium on security and privacy (sp), pp. 39-57. IEEE, 2017.
Carmon, Y., Raghunathan, A., Schmidt, L., Duchi, J. C., and Liang, P. S. Unlabeled data improves adversarial robustness. Advances in Neural Information Processing Systems, 32, 2019.
Chen, P., Liu, S., Zhao, H., and Jia, J. Gridmask data augmentation. arXiv preprint arXiv:2001.04086, 2020.
Cohen, J., Rosenfeld, E., and Kolter, Z. Certified adversarial robustness via randomized smoothing. In International Conference on Machine Learning, pp. 1310-1320. PMLR, 2019.
Croce, F. and Hein, M. Minimally distorted adversarial examples with a fast adaptive boundary attack. In International Conference on Machine Learning, pp. 2196-2205. PMLR, 2020a.
Croce, F. and Hein, M. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning, pp. 2206-2216. PMLR, 2020b.
Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., and Hein, M. Robustbench: a standardized adversarial robustness benchmark. arXiv preprint arXiv:2010.09670, 2020.
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V. Autoaugment: Learning augmentation strategies from data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 113-123, 2019.
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 702-703, 2020.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-255. IEEE, 2009.
DeVries, T. and Taylor, G. W. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552, 2017.