[102] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In CVPR, 2019.
[103] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016.
[104] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence, 2017.
[105] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019.
[106] Sahil Verma and Julia Rubin. Fairness definitions explained. In 2018 IEEE/ACM International Workshop on Software Fairness (FairWare), pages 1-7. IEEE, 2018.
[107] Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh. Cspnet: A new backbone that can enhance learning capability of cnn. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 390–391, 2020.
[108] Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu. Cosface: Large margin cosine loss for deep face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5265-5274, 2018.
[109] Mei Wang and Weihong Deng. Deep face recognition: A survey. arXiv preprint arXiv:1804.06655, 2018.
[110] Mei Wang and Weihong Deng. Mitigating bias in face recognition using skewness-aware reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9322-9331, 2020.
[111] Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao, and Yaohai Huang. Racial faces in the wild: Reducing racial bias by information maximization adaptation network. In Proceedings of the IEEE International Conference on Computer Vision, pages 692-702, 2019.
[112] Qingzhong Wang, Pengfei Zhang, Haoyi Xiong, and Jian Zhao. Face. evolve: A high-performance face recognition library. arXiv preprint arXiv:2107.08621, 2021.
[113] Xiaobo Wang. Teacher guided neural architecture search for face recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 2817-2825, 2021.
[114] Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky. Towards fairness in visual recognition: Effective strategies for bias mitigation, 2020.
[115] Colin White, Willie Neiswanger, and Yash Savani. Bananas: Bayesian optimization with neural architectures for neural architecture search. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 10293–10301, 2021.
[116] Colin White, Mahmoud Safari, Rhea Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, and Frank Hutter. Neural architecture search: Insights from 1000 papers. arXiv preprint arXiv:2301.08727, 2023.
[117] Ross Wightman. Pytorch image models. https://github.com/rwrightman/pytorch-image-models, 2019.
[118] Wenying Wu, Pavlos Protopapas, Zheng Yang, and Panagiotis Michalatos. Gender classification and bias mitigation in facial images. In 12th acm conference on web science, pages 106-114, 2020.
[119] Saining Xie, Ross B. Girshick, Piotr Dólar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. CoRR, abs/1611.05431, 2016. URL http://arxiv.org/abs/1611.05431.