[23] Chuanwen Feng, Yilong Ren, and Xike Xie. Ot-filter: An optimal transport filter for learning with noisy labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16164-16174, 2023.
[24] Sira Ferradans, Nicolas Papadakis, Gabriel Peyre, and Jean-François Aujol. Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3):1853-1882, 2014.
[25] Enrico Fini, Enver Sangineto, Stéphane Lathuilière, Zhun Zhong, Moin Nabi, and Elisa Ricci. A unified objective for novel class discovery. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9284-9292, 2021.
[26] Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflós, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavernard, Alexander Tong, and Titouan Vayer. Pot: Python optimal transport. Journal of Machine Learning Research, 22(78):1-8, 2021.
[27] Marguerite Frank and Philip Wolfe. An algorithm for quadratic programming. Naval research logistics quarterly, 3(1-2):95-110, 1956.
[28] Dandan Guo, Zhuo Li, He Zhao, Mingyuan Zhou, Hongyuan Zha, et al. Learning to re-weight examples with optimal transport for imbalanced classification. Advances in Neural Information Processing Systems, 35:25517-25530, 2022.
[29] Lan-Zhe Guo and Yu-Feng Li. Class-imbalanced semi-supervised learning with adaptive thresholding. In International Conference on Machine Learning, pages 8082-8094. PMLR, 2022.
[30] Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang. Curriculumnet: Weakly supervised learning from large-scale web images. In Proceedings of the European conference on computer vision, pages 135-150, 2018.
[31] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems, 31, 2018.
[32] Kaiming He, Georgia Gkioxari, Piotr Dólár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017.
[33] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[34] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In European Conference on Computer Vision, pages 630-645. Springer, 2016.
[35] Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. Advances in neural information processing systems, 31, 2018.
[36] Martin Jaggi. Revisiting frank-wolfe: Projection-free sparse convex optimization. In International conference on machine learning, pages 427-435. PMLR, 2013.
[37] Lu Jiang, Di Huang, Mason Liu, and Weilong Yang. Beyond synthetic noise: Deep learning on controlled noisy labels. In International conference on machine learning, pages 4804-4815. PMLR, 2020.
[38] Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander Hauptmann. Self-paced curriculum learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29, 2015.
[39] Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International conference on machine learning, pages 2304–2313. PMLR, 2018.
[40] Leonid V Kantorovich. On the translocation of masses. Journal of mathematical sciences, 133(4):1381-1382, 2006.
[41] Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard, Ajmal Mian, and Mubarak Shah. Unicon: Combating label noise through uniform selection and contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9676-9686, 2022.
[42] Faisal Khan, Bilge Mutlu, and Jerry Zhu. How do humans teach: On curriculum learning and teaching dimension. Advances in neural information processing systems, 24, 2011.
[43] Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. 2009.
[44] Zhengfeng Lai, Chao Wang, Sen-ching Cheung, and Chen-Nee Chuah. Sar: Self-adaptive refinement on pseudo labels for multiclass-imbalanced semi-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091–4100, 2022.