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Learning with noisy labels has attracted a lot of attention in recent years, where the mainstream approaches are in pointwise manners.
The mnist database of handwritten digits
LeCun, Y · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
Earlier work this paper cites.
Multi-class classification from noisy-similarity-labeled data
Wu, S., Xia, X., Liu, T., Han, B., Gong, M., Wang, N., Liu, H., and Niu, G · 2002
Earlier work this paper cites.
On the consistency of multiclass classification methods
Tewari, A. and Bartlett, P. L · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
A survey of label-noise representation learning: Past, present and future
Han, B., Yao, Q., Liu, T., Niu, G., Tsang, I. W., Kwok, J. T., and Sugiyama, M · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
Earlier work this paper cites.
Learning from corrupted binary labels via class-probability estimation
Menon, A., Van Rooyen, B., Ong, C. S., and Williamson, B · 2015
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Reed, S. E., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2015
Earlier work this paper cites.
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
Earlier work this paper cites.
Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2016
Earlier work this paper cites.
Gastaldi, X · 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
Earlier work this paper cites.
Learning with confident examples: Rank pruning for robust classification with noisy labels
Northcutt, C. G., Wu, T., and Chuang, I. L · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Toward robustness against label noise in training deep discriminative neural networks
Vahdat, A · 2017
Earlier work this paper cites.
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.
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 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.
Learning to cluster in order to transfer across domains and tasks
Hsu, Y.-C., Lv, Z., and Kira, Z · 2018
Cited alongside, same era.
MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection
Zhong, J.-X., Li, N., Kong, W., Liu, S., Li, T. H., and Li, G · 2019
Later among the works it cites.
Similarity-based classification: Connecting similarity learning to binary classification
Bao, H., Shimada, T., Xu, L., Sato, I., and Sugiyama, M · 2020
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A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Boudiaf, M., Rony, J., Ziko, I. M., Granger, E., Pedersoli, M., Piantanida, P., and Ayed, I. B · 2020
Closest in time.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Closest in time.
Learning with bounded instance-and label-dependent label noise
Cheng, J., Liu, T., Ramamohanarao, K., and Tao, D · 2020
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Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L · 2018
Cited alongside, same era.
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.
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A · 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.
Learning with biased complementary labels
Yu, X., Liu, T., Gong, M., and Tao, D · 2018
Cited alongside, same era.
Unbiased risk estimators can mislead: A case study of learning with complementary labels
Chou, Y.-T., Niu, G., Lin, H.-T., and Sugiyama, M · 2020
Closest in time.
Learning with multiple complementary labels
Feng, L., Kaneko, T., Han, B., Niu, G., An, B., and Sugiyama, M · 2020
Closest in time.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Closest in time.
Simple and effective regularization methods for training on noisily labeled data with generalization guarantee
Hu, W., Li, Z., and Yu, D · 2020
Closest in time.
Peer loss functions: Learning from noisy labels without knowing noise rates
Liu, Y. and Guo, H · 2020
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Normalized loss functions for deep learning with noisy labels
Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S., and Bailey, J · 2020
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Can gradient clipping mitigate label noise?
Menon, A. K., Rawat, A. S., Reddi, S. J., and Kumar, S · 2020
Closest in time.
Combating noisy labels by agreement: A joint training method with co-regularization
Wei, H., Feng, L., Chen, X., and An, B · 2020
Closest in time.
Label-noise robust domain adaptation
Yu, X., Liu, T., Gong, M., Zhang, K., Batmanghelich, K., and Tao, D · 2020
Closest in time.
Error-bounded correction of noisy labels
Zheng, S., Wu, P., Goswami, A., Goswami, M., Metaxas, D., and Chen, C · 2020
Closest in time.
Confidence scores make instance-dependent label-noise learning possible
Berthon, A., Han, B., Niu, G., Liu, T., and Sugiyama, M · 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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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 noise transition matrix from only noisy labels via total variation regularization
Zhang, Y., Niu, G., and Sugiyama, M · 2021
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A second-order approach to learning with instance-dependent label noise
Zhu, Z., Liu, T., and Liu, Y · 2021
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