[14] Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey. Dimensionality-driven learning with noisy labels. In International Conference on Machine Learning, pages 3355-3364. PMLR, 2018.
[15] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 2022.
[16] 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 European Conference on Computer Vision, pages 135-150, 2018.
[17] 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.
[18] Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang. Cleannet: Transfer learning for scalable image classifier training with label noise. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5447-5456, 2018.
[19] Yulei Qin, Xingyu Chen, Chao Chen, Yunhang Shen, Bo Ren, Yun Gu, Jie Yang, and Chunhua Shen. Fopro: Few-shot guided robust webly-supervised prototypical learning. arXiv preprint arXiv:2212.00465, 2022.
[20] 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.
[21] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5552-5560, 2018.
[22] Jingkang Yang, Litong Feng, Weirong Chen, Xiaopeng Yan, Huabin Zheng, Ping Luo, and Wayne Zhang. Webly supervised image classification with self-contained confidence. In European Conference on Computer Vision, pages 779-795. Springer, 2020.
[23] Xiangzeng Zhou, Pan Pan, Yun Zheng, Yinghui Xu, and Rong Jin. Large scale long-tailed product recognition system at alibaba. In Proceedings of ACM International Conference on Information & Knowledge Management, pages 3353-3356, 2020.
[24] Lele Cheng, Xiangzeng Zhou, Liming Zhao, Dangwei Li, Hong Shang, Yun Zheng, Pan Pan, and Yinghui Xu. Weakly supervised learning with side information for noisy labeled images. In European Conference on Computer Vision, pages 306-321. Springer, 2020.
[25] Junnan Li, Richard Socher, and Steven CH Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In International Conference on Learning Representations, 2019.
[26] Sai Rajeswar, Pau Rodriguez, Soumye Singhal, David Vazquez, and Aaron Courville. Multi-label iterated learning for image classification with label ambiguity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4783-4793, 2022.
[27] Junnan Li, Pan Zhou, Caiming Xiong, and Steven Hoi. Prototypical contrastive learning of unsupervised representations. In International Conference on Learning Representations, 2020.
[28] Junnan Li, Caiming Xiong, and Steven Hoi. Mopro: Webly supervised learning with momentum prototypes. In International Conference on Learning Representations, 2020.
[29] Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems, 32, 2019.
[30] Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems, 33:5776-5788, 2020.