[18] Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, and Hongsheng Li. Balanced meta-softmax for long-tailed visual recognition. In Annual Conference on Neural Information Processing Systems, 2020.
[19] Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. The MIT Press, 2012.
[20] Michel Ledoux and Michel Talagrand. Probability in Banach Spaces: isoperimetry and processes, volume 23. Springer Science & Business Media, 1991.
[21] Aditya Krishna Menon, Harikrishna Narasimhan, Shivani Agarwal, and Sanjay Chawla. On the statistical consistency of algorithms for binary classification under class imbalance. In International Conference on Machine Learning, pages 603-611, 2013.
[22] Sham M. Kakade, Karthik Sridharan, and Ambuj Tewari. On the complexity of linear prediction: Risk bounds, margin bounds, and regularization. In Annual Conference on Neural Information Processing Systems, pages 793-800, 2008.
[23] Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson. Localized rademacher complexities. In Annual Conference on Computational Learning Theory, pages 44-58, 2002.
[24] Noah Golowich, Alexander Rakhlin, and Ohad Shamir. Size-independent sample complexity of neural networks. In Conference On Learning Theory, pages 297-299, 2018.
[25] Philip M. Long and Hanie Sedghi. Generalization bounds for deep convolutional neural networks. In International Conference on Learning Representations, 2020.
[26] Mateusz Buda, Atsuto Maki, and Maciej A. Mazurowski. A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106:249-259, 2018.
[27] Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X. Yu. Large-scale long-tailed recognition in an open world. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2537-2546, 2019.
[28] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2021.
[29] Harsh Rangwani, Sumukh K. Aithal, Mayank Mishra, and R. Venkatesh Babu. Escaping saddle points for effective generalization on class-imbalanced data. In Advances in Neural Information Processing Systems, pages 22791-22805, 2022.
[30] Liuyu Xiang, Guiguang Ding, and Jungong Han. Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. In European Conference on Computer Vision, pages 247-263, 2020.
[31] Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen. BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9716–9725, 2020.
[32] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classifier for long-tailed recognition. In International Conference on Learning Representations, 2020.
[33] Yin-Yin He, Jianxin Wu, and Xiu-Shen Wei. Distilling virtual examples for long-tailed recognition. In IEEE/CVF International Conference on Computer Vision, pages 235-244, 2021.
[34] Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, and Jian Sun. Distribution alignment: A unified framework for long-tail visual recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2361-2370, 2021.
[35] Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, and Shu Kong. Long-tailed recognition via weight balancing. In IEEE/CVF Conference on Computer Vision and Pattern Recognition., pages 6887-6897, 2022.