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ACM, vol.59, no.2, pp.64-73, Feb. 2016. 10.1145\/2812802","DOI":"10.1145\/2812802"},{"key":"5","unstructured":"[5] D. Arpit, S. Jastrz\u0119bski, N. Ballas, D. Krueger, E. Bengio, M.S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, and S. Lacoste-Julien, \u201cA closer look at memorization in deep networks,\u201d Int. Conf. Mach. Learn., vol.70, pp.233-242, PMLR, Aug. 2017."},{"key":"6","unstructured":"[6] X. Ma, Y. Wang, M.E. Houle, S. Zhou, S. Erfani, S. Xia, S. Wijewickrema, and J. Bailey, \u201cDimensionality-driven learning with noisy labels,\u201d Int. Conf. Mach. Learn., pp.3355-3364, 2018."},{"key":"7","unstructured":"[7] T. Kim, J. Ko, S. Cho, J. Choi, and S.Y. Yun, \u201cFine samples for learning with noisy labels,\u201d Advances in Neural Information Processing Systems, vol.34, pp.24137-24149, 2021."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] A. Ghosh, H. Kumar, and P. Sastry, \u201cRobust loss functions under label noise for deep neural networks,\u201d Proc. AAAI Conf. Artifi. Intelli., 2017. 10.1609\/aaai.v31i1.10894","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"9","unstructured":"[9] Z. Zhang and M.R. Sabuncu, \u201cGeneralized cross entropy loss for training deep neural networks with noisy labels,\u201d arXiv preprint arXiv:1805.07836, 2018. 10.48550\/arXiv.1805.07836"},{"key":"10","unstructured":"[10] S. Liu, J. Niles-Weed, N. Razavian, and C. Fernandez-Granda, \u201cEarly-learning regularization prevents memorization of noisy labels,\u201d Advances in Neural Information Processing Systems, vol.33, pp.20331-20342, 2020."},{"key":"11","unstructured":"[11] J. Goldberger and E. Ben-Reuven, \u201cTraining deep neural-networks using a noise adaptation layer,\u201d Int. Conf. Learning Representations, 2017."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu, \u201cMaking deep neural networks robust to label noise: A loss correction approach,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp.1944-1952, 2017. 10.1109\/CVPR.2017.240","DOI":"10.1109\/CVPR.2017.240"},{"key":"13","unstructured":"[13] B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, \u201cCo-teaching: Robust training of deep neural networks with extremely noisy labels,\u201d Advances in Neural Information Processing Systems, pp.8535-8545, 2018."},{"key":"14","unstructured":"[14] X. Yu, B. Han, J. Yao, G. Niu, I. Tsang, and M. Sugiyama, \u201cHow does disagreement help generalization against label corruption?,\u201d Int. Conf. Mach. Learn., pp.7164-7173, PMLR, 2019."},{"key":"15","unstructured":"[15] P. Wu, S. Zheng, M. Goswami, D. Metaxas, and C. Chen, \u201cA topological filter for learning with label noise,\u201d Advances in Neural Information Processing Systems, pp.21382-21393, 2020."},{"key":"16","unstructured":"[16] S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich, \u201cTraining deep neural networks on noisy labels with bootstrapping,\u201d Int. Conf. Learning Representations, 2015."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] D. Tanaka, D. Ikami, T. Yamasaki, and K. Aizawa, \u201cJoint optimization framework for learning with noisy labels,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp.5552-5560, 2018. 10.1109\/CVPR.2018.00582","DOI":"10.1109\/CVPR.2018.00582"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] K. Yi and J. Wu, \u201cProbabilistic end-to-end noise correction for learning with noisy labels,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp.7017-7025, 2019. 10.1109\/CVPR.2019.00718","DOI":"10.1109\/CVPR.2019.00718"},{"key":"19","unstructured":"[19] E. Arazo, D. Ortego, P. Albert, N. O'Connor, and K. McGuinness, \u201cUnsupervised label noise modeling and loss correction,\u201d Int. Conf. Mach. 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