1. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning (still) requires rethinking generalization. Commun. ACM 64(3), 107–115 (2021)
2. Liu, S., Niles-Weed, J., Razavian, N., Fernandez-Granda, C.: Early-learning regularization prevents memorization of noisy labels. NeurIPS 33, 20331–20342 (2020)
3. Li, J., Socher, R., Hoi, S.C.H.: DivideMix: learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020)
4. Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: NeurIPS, vol. 31 (2018)
5. Bai, Y., Liu, T.: Me-momentum: extracting hard confident examples from noisily labeled data. In: ICCV, pp. 9312–9321 (2021)