[51] Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar. A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2239-2248, 2018.
[52] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929-1958, 2014.
[53] Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer. How to train your vit? data, augmentation, and regularization in vision transformers. arXiv preprint arXiv:2106.10270, 2021.
[54] Emma Strubell, Ananya Ganesh, and Andrew McCallum. Energy and policy considerations for deep learning in NLP. In Anna Korhonen, David R. Traum, and Lluis Marquez, editors, Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pages 3645-3650. Association for Computational Linguistics, 2019.
[55] Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. A survey on deep transfer learning. In International conference on artificial neural networks, pages 270-279. Springer, 2018.
[56] Songül Tolan, Marius Miron, Emilia Gómez, and Carlos Castillo. Why machine learning may lead to unfairness: Evidence from risk assessment for juvenile justice in catalonia. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law, pages 83–92, 2019.
[57] Michael Veale, Max Van Kleek, and Reuben Binns. Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 chi conference on human factors in computing systems, pages 1-14, 2018.
[58] Zeming Wei, Yifei Wang, Yiwen Guo, and Yisen Wang. Cfa: Class-wise calibrated fair adversarial training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8193-8201, 2023.
[59] Ross Wightman. Pytorch image models. https://github.com/rwrightman/pytorch-image-models, 2019.
[60] Ross Wightman, Hugo Touvron, and Hervé Jégou. Resnet strikes back: An improved training procedure in timm. abs/2110.00476, 2021.
[61] Eric Wong, Shibani Santurkar, and Aleksander Madry. Leveraging sparse linear layers for debuggable deep networks. In International Conference on Machine Learning, pages 11205-11216. PMLR, 2021.
[62] Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang. To be robust or to be fair: Towards fairness in adversarial training. In International conference on machine learning, pages 11492-11501. PMLR, 2021.
[63] Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. Understanding neural networks through deep visualization, 2015.
[64] Jiahui Yu and Thomas S Huang. Universally slimmable networks and improved training techniques. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1803-1811, 2019.
[65] Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas S. Huang. Slimmable neural networks. abs/1812.08928, 2018.
[66] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. Fairness constraints: Mechanisms for fair classification. In Artificial intelligence and statistics, pages 962-970. PMLR, 2017.