[50] R. Vedantam, D. Lopez-Paz, and D. J. Schwab, “An empirical investigation of domain generalization with empirical risk minimizers,” Advances in Neural Information Processing Systems, vol. 34, pp. 28 131–28 143, 2021.
[51] M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning. MIT press, 2018.
[52] Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in International conference on machine learning. PMLR, 2015, pp. 1180–1189.
[53] A. Rame, C. Dancette, and M. Cord, "Fishr: Invariant gradient variances for out-of-distribution generalization," in International Conference on Machine Learning. PMLR, 2022, pp. 18347-18377.
[54] 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, 2016, pp. 770–778.
[55] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., "Imagenet large scale visual recognition challenge," International journal of computer vision, vol. 115, pp. 211-252, 2015.
[56] N. Qian, "On the momentum term in gradient descent learning algorithms," Neural networks, vol. 12, no. 1, pp. 145-151, 1999.
[57] V. N. Vapnik, “An overview of statistical learning theory,” IEEE transactions on neural networks, vol. 10, no. 5, pp. 988–999, 1999.
[58] B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in Computer Vision-ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14. Springer, 2016, pp. 443-450.
[59] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, "Domain-adversarial training of neural networks," The journal of machine learning research, vol. 17, no. 1, pp. 2096-2030, 2016.
[60] Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao, “Deep domain generalization via conditional invariant adversarial networks,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 624–639.
[61] D. Li, Y. Yang, Y.-Z. Song, and T. Hospedales, “Learning to generalize: Meta-learning for domain generalization,” in Proceedings of the AAAI conference on artificial intelligence, vol. 32, no. 1, 2018.
[62] H. Li, S. J. Pan, S. Wang, and A. C. Kot, "Domain generalization with adversarial feature learning," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 5400-5409.
[63] M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz, "Invariant risk minimization," arXiv preprint arXiv:1907.02893, 2019.
[64] S. Sagawa, P. W. Koh, T. B. Hashimoto, and P. Liang, "Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization," arXiv preprint arXiv:1911.08731, 2019.
[65] S. Yan, H. Song, N. Li, L. Zou, and L. Ren, "Improve unsupervised domain adaptation with mixup training," arXiv preprint arXiv:2001.00677, 2020.
[66] M. Zhang, H. Marklund, A. Gupta, S. Levine, and C. Finn, "Adaptive risk minimization: A meta-learning approach for tackling group shift," arXiv preprint arXiv:2007.02931, vol. 8, p. 9, 2020.
[67] Z. Huang, H. Wang, E. P. Xing, and D. Huang, "Self-challenging improves cross-domain generalization," in Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16. Springer, 2020, pp. 124-140.