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Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels.
A stochastic approximation method
Robbins, H. and Monro, S · 1951
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Extended T: Learning with mixed closed-set and open-set noisy labels
Xia, X., Liu, T., Han, B., Wang, N., Deng, J., Li, J., and Mao, Y · 2012
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Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
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Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., Handy, G., Pozzi, S., and Flaska, M · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2015
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Learning from corrupted binary labels via class-probability estimation
Menon, A., Van Rooyen, B., Ong, C. S., and Williamson, B · 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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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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Label distribution learning
Geng, X · 2016
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Mixture proportion estimation via kernel embeddings of distributions
Harish, R., Scott, C., and Tewari, A · 2016
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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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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Towards better decoding and language model integration in sequence to sequence models
Chorowski, J. and Jaitly, N · 2017
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Learning from complementary labels
Ishida, T., Niu, G., Hu, W., and Sugiyama, M · 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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Regularizing neural networks by penalizing confident output distributions
Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., and Hinton, G · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M · 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.
Robust bi-tempered logistic loss based on bregman divergences
Amid, E., Warmuth, M. K., Anil, R., and Koren, T · 2019
Cited alongside, same era.
Understanding and improving early stopping for learning with noisy labels
Bai, Y., Yang, E., Han, B., Yang, Y., Li, J., Mao, Y., Niu, G., and Liu, T · 2021
Closest in time.
Confidence scores make instance-dependent label-noise learning possible
Berthon, A., Han, B., Niu, G., Liu, T., and Sugiyama, M · 2021
Closest in time.
Rethinking noisy label models: Labeler-dependent noise with adversarial awareness
Dawson, G. and Polikar, R · 2021
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Constrained instance and class reweighting for robust learning under label noise
Kumar, A. and Amid, E · 2021
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Convolutional normalization: Improving deep convolutional network robustness and training
Liu, S., Li, X., Zhai, Y., You, C., Zhu, Z., Fernandez-Granda, C., and Qu, Q · 2021
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Complementary-label learning for arbitrary losses and models
Ishida, T., Niu, G., Menon, A., and Sugiyama, M · 2019
Cited alongside, same era.
Nlnl: Negative learning for noisy labels
Kim, Y., Yim, J., Yun, J., and Kim, J · 2019
Cited alongside, same era.
When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G · 2019
Cited alongside, same era.
Symmetric cross entropy for robust learning with noisy labels
Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., and Bailey, J · 2019
Cited alongside, same era.
How does disagreement help generalization against label corruption?
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., and Sugiyama, M · 2019
Cited alongside, same era.
Provably consistent partial-label learning
Feng, L., Lv, J., Han, B., Xu, M., Niu, G., Geng, X., An, B., and Sugiyama, M · 2020
Cited alongside, same era.
Regularization via structural label smoothing
Li, W., Dasarathy, G., and Berisha, V · 2020
Cited alongside, same era.
Understanding instance-level label noise: Disparate impacts and treatments
Liu, Y · 2021
Closest in time.
Can less be more? when increasing-to-balancing label noise rates considered beneficial
Liu, Y. and Wang, J · 2021
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On the robustness of average losses for partial-label learning
Lv, J., Feng, L., Xu, M., An, B., Niu, G., Geng, X., and Sugiyama, M · 2021
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Exponentiated gradient reweighting for robust training under label noise and beyond
Majidi, N., Amid, E., Talebi, H., and Warmuth, M. K · 2021
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Confident learning: Estimating uncertainty in dataset labels
Northcutt, C., Jiang, L., and Chuang, I · 2021
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Fair classification with group-dependent label noise
Wang, J., Liu, Y., and Levy, C · 2021
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Open-set label noise can improve robustness against inherent label noise
Wei, H., Tao, L., Xie, R., and An, B · 2021
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When optimizing f f -divergence is robust with label noise
Wei, J. and Liu, Y · 2021
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Estimating instance-dependent label-noise transition matrix using dnns
Yang, S., Yang, E., Han, B., Liu, Y., Xu, M., Niu, G., and Liu, T · 2021
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Rethinking soft labels for knowledge distillation: A bias–variance tradeoff perspective
Zhou, H., Song, L., Chen, J., Zhou, Y., Wang, G., Yuan, J., and Zhang, Q · 2021
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Instance-dependent label-noise learning with manifold-regularized transition matrix estimation
Cheng, D., Liu, T., Ning, Y., Wang, N., Han, B., Niu, G., Gao, X., and Sugiyama, M · 2022
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An information fusion approach to learning with instance-dependent label noise
Jiang, Z., Zhou, K., Liu, Z., Li, L., Chen, R., Choi, S.-H., and Hu, X · 2022
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Robust training under label noise by over-parameterization
Liu, S., Zhu, Z., Qu, Q., and You, C · 2022
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PiCO: Contrastive label disambiguation for partial label learning
Wang, H., Xiao, R., Li, Y., Feng, L., Niu, G., Chen, G., and Zhao, J · 2022
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On learning contrastive representations for learning with noisy labels
Yi, L., Liu, S., She, Q., McLeod, A. I., and Wang, B · 2022
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Beyond images: Label noise transition matrix estimation for tasks with lower-quality features
Zhu, Z., Wang, J., and Liu, Y · 2022
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