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In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers.
Learning from noisy examples
Angluin, D. and Laird, P · 1988
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Noise-tolerant learning, the parity problem, and the statistical query model
Blum, A., Kalai, A., and Wasserman, H · 2003
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Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D · 2006
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Support vector machines under adversarial label noise
Biggio, B., Nelson, B., and Laskov, P · 2011
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Noise tolerance under risk minimization
Manwani, N. and Sastry, P · 2013
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Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
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Learning from multiple annotators with varying expertise
Yan, Y., Rosales, R., Fung, G., Subramanian, R., and Dy, J · 2014
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
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Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2016
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Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2017
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Learning from noisy labels with distillation
Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., and Li, L.-J · 2017
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Algorithmic stability and hypothesis complexity
Liu, T., Lugosi, G., Neu, G., and Tao, D · 2017
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Decoupling” when to update” from” how to update”
Malach, E. and Shalev-Shwartz, S · 2017
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Learning with confident examples: Rank pruning for robust classification with noisy labels
Northcutt, C. G., Wu, T., and Chuang, I. L · 2017
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Making deep neural networks robust to label noise: A loss correction approach
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L · 2017
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Toward robustness against label noise in training deep discriminative neural networks
Vahdat, A · 2017
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Learning from noisy large-scale datasets with minimal supervision
Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., and Belongie, S · 2017
Cited alongside, same era.
Transfer learning with label noise
Yu, X., Liu, T., Gong, M., Zhang, K., Batmanghelich, K., and Tao, D · 2017
Cited alongside, same era.
Curriculumnet: Weakly supervised learning from large-scale web images
Guo, S., Huang, W., Zhang, H., Zhuang, C., Dong, D., Scott, M. R., and Huang, D · 2018
Cited alongside, same era.
Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, D., Mazeika, M., Wilson, D., and Gimpel, K · 2018
Cited alongside, same era.
MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L · 2018
Cited alongside, same era.
Sigua: Forgetting may make learning with noisy labels more robust
Han, B., Niu, G., Yu, X., Yao, Q., Xu, M., Tsang, I. W., and Sugiyama, M · 2020
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Early-learning regularization prevents memorization of noisy labels
Liu, S., Niles-Weed, J., Razavian, N., and Fernandez-Granda, C · 2020
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Peer loss functions: Learning from noisy labels without knowing noise rates
Liu, Y. and Guo, H · 2020
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Curriculum loss: Robust learning and generalization against label corruption
Lyu, Y. and Tsang, I. W · 2020
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Normalized loss functions for deep learning with noisy labels
Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S. M., and Bailey, J · 2020
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Self: Learning to filter noisy labels with self-ensembling
Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., and Brox, T · 2020
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Robust active label correction
Kremer, J., Sha, F., and Igel, C · 2018
Cited alongside, same era.
Dimensionality-driven learning with noisy labels
Ma, X., Wang, Y., Houle, M. E., Zhou, S., Erfani, S. M., Xia, S.-T., Wijewickrema, S., and Bailey, J · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
Cited alongside, same era.
Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
Cited alongside, same era.
Robustness of conditional gans to noisy labels
Thekumparampil, K. K., Khetan, A., Lin, Z., and Oh, S · 2018
Cited alongside, same era.
Learning with biased complementary labels
Yu, X., Liu, T., Gong, M., and Tao, D · 2018
Cited alongside, same era.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
Cited alongside, same era.
Later among the works it cites.
Meta transition adaptation for robust deep learning with noisy labels
Shu, J., Zhao, Q., Xu, Z., and Meng, D · 2020
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Class2simi: A new perspective on learning with label noise
Wu, S., Xia, X., Liu, T., Han, B., Gong, M., Wang, N., Liu, H., and Niu, G · 2020
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Error-bounded correction of noisy labels
Zheng, S., Wu, P., Goswami, A., Goswami, M., Metaxas, D., and Chen, C · 2020
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A second-order approach to learning with instance-dependent label noise
Zhu, Z., Liu, T., and Liu, Y · 2020
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Learning with instance-dependent label noise: A sample sieve approach
Cheng, H., Zhu, Z., Li, X., Gong, Y., Sun, X., and Liu, Y · 2021
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Provably end-to-end label-noise learning without anchor points
Li, X., Liu, T., Han, B., Niu, G., and Sugiyama, M · 2021
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Proselflc: Progressive self label correction for training robust deep neural networks
Wang, X., Hua, Y., Kodirov, E., Clifton, D. A., and Robertson, N. M · 2021
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Robust early-learning: Hindering the memorization of noisy labels
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., and Chang, Y · 2021
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Learning with feature-dependent label noise: A progressive approach
Zhang, Y., Zheng, S., Wu, P., Goswami, M., and Chen, C · 2021
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Meta label correction for noisy label learning
Zheng, G., Awadallah, A. H., and Dumais, S. T · 2021
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Clusterability as an alternative to anchor points when learning with noisy labels
Zhu, Z., Song, Y., and Liu, Y · 2021
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Reliable label correction is a good booster when learning with extremely noisy labels
Wang, K., Peng, X., Yang, S., Yang, J., Zhu, Z., Wang, X., and You, Y · 2022
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