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In supervised machine learning, use of correct labels is extremely important to ensure high accuracy.
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H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” in Proc. Int. Conf. Learn. Represent. , 2018
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2018
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D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel, “Using trusted data to train deep networks on labels corrupted by severe noise,” in Adv. Neural Inf. Process. Syst. , vol. 31, 2018
2018
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M. Ren, W. Zeng, B. Yang, and R. Urtasun, “Learning to reweight examples for robust deep learning,” in Proc. Int. Conf. Mach. Learn. , 2018, pp. 4334–4343
2018
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J. Shu, Q. Xie, L. Yi, Q. Zhao, S. Zhou, Z. Xu, and D. Meng, “Meta-Weight-Net: Learning an explicit mapping for sample weighting,” in Adv. Neural Inf. Process. Syst. , vol. 32, 2018
2018
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Z. Lipton, Y.-X. Wang, and A. Smola, “Detecting and correcting for label shift with black box predictors,” in Proc. Int. Conf. Mach. Learn. , 2018, pp. 3122–3130
2018
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K.-H. Lee, X. He, L. Zhang, and L. Yang, “CleanNet: Transfer learning for scalable image classifier training with label noise,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit. , 2018, pp. 5447–5456
2018
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2020
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M. Li, M. Soltanolkotabi, and S. Oymak, “Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks,” in Proc. Int. Conf. Artif. Intell. Statist. , 2020, pp. 4313–4324
2020
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X. Ma, H. Huang, Y. Wang, S. Romano, S. Erfani, and J. Bailey, “Normalized loss functions for deep learning with noisy labels,” in Proc. Int. Conf. Mach. Learn. , 2020, pp. 6543–6553
2020
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J. Li, R. Socher, and S. C. Hoi, “DivideMix: Learning with noisy labels as semi-supervised learning,” in Proc. Int. Conf. Learn. Represent. , 2020
2020
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C. G. Northcutt, A. Athalye, and J. Mueller, “Pervasive label errors in test sets destabilize machine learning benchmarks,” in NeurIPS Datasets and Benchmarks Track (Round 1) , 2021
2021
Later among the works it cites.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,” Commun. ACM , vol. 64, no. 3, pp. 107–115, 2021
2021
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C. Northcutt, L. Jiang, and I. Chuang, “Confident learning: Estimating uncertainty in dataset labels,” J. Artif. Intell. Res. , vol. 70, pp. 1373–1411, 2021
2021
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C. Wang, J. Shi, Y. Zhou, L. Li, X. Yang, T. Zhang, S. Wei, X. Zhang, and C. Tao, “Label noise modeling and correction via loss curve fitting for SAR ATR,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–10, 2022
2022
Closest in time.
S. Jiang, J. Li, Y. Wang, B. Huang, Z. Zhang, and T. Xu, “Delving into sample loss curve to embrace noisy and imbalanced data,” in Proc. Conf. AAAI Artif. Intell. , vol. 36, no. 6, 2022
2022
Closest in time.