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Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem.
Transformer-xl: Attentive language models beyond a fixed-length context
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Unsupervised data augmentation for consistency training
Xie, Q.; Dai, Z.; Hovy, E.; Luong, M.-T.; and Le, Q. V. 2019 · 1904
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Meta-weight-net: Learning an explicit mapping for sample weighting
Shu, J.; Xie, Q.; Yi, L.; Zhao, Q.; Zhou, S.; Xu, Z.; and Meng, D. 2019 · 1928
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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 · 1952
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Design of robust neural network classifiers
Larsen, J.; Nonboe, L.; Hintz-Madsen, M.; and Hansen, L. K. 1998 · 1998
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Pham, H.; Xie, Q.; Dai, Z.; and Le, Q. V. 2020 · 2003
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Learning multiple layers of features from tiny images
Krizhevsky, A. 2009 · 2009
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A study of the effect of different types of noise on the precision of supervised learning techniques
Nettleton, D. F.; Orriols-Puig, A.; and Fornells, A. 2010 · 2010
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Learning to label aerial images from noisy data
Mnih, V.; and Hinton, G. E. 2012 · 2012
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Classification in the presence of label noise: a survey
Frénay, B.; and Verleysen, M. 2013 · 2013
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Learning with noisy labels
Natarajan, N.; Dhillon, I. S.; Ravikumar, P. K.; and Tewari, A. 2013 · 2013
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 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 · 2014
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Gradient-based hyperparameter optimization through reversible learning
Maclaurin, D.; Duvenaud, D.; and Adams, R. 2015 · 2015
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Sequence level training with recurrent neural networks
Ranzato, M.; Chopra, S.; Auli, M.; and Zaremba, W. 2015 · 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 · 2015
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Character-level convolutional networks for text classification
Zhang, X.; Zhao, J.; and LeCun, Y. 2015 · 2015
Cited alongside, same era.
Very deep convolutional networks for text classification
Conneau, A.; Schwenk, H.; Barrault, L.; and Lecun, Y. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Hyperparameter optimization with approximate gradient
Pedregosa, F. 2016 · 2016
Cited alongside, same era.
A brief introduction to weakly supervised learning
Zhou, Z.-H. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, D.; Mazeika, M.; Wilson, D.; and Gimpel, K. 2018 · 2018
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On first-order meta-learning algorithms
Nichol, A.; Achiam, J.; and Schulman, J. 2018 · 2018
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Learning to Reweight Examples for Robust Deep Learning
Ren, M.; Zeng, W.; Yang, B.; and Urtasun, R. 2018 · 2018
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Yang, Z.; Yang, D.; Dyer, C.; He, X.; Smola, A.; and Hovy, E. 2016 · 2016
Cited alongside, same era.
Learning from untrusted data
Charikar, M.; Steinhardt, J.; and Valiant, G. 2017 · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Goldberger, J.; and Ben-Reuven, E. 2017 · 2017
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. 2017 · 2017
Cited alongside, same era.
Learning From Noisy Labels With Distillation
Li, Y.; Yang, J.; Song, Y.; Cao, L.; Luo, J.; and Li, L.-J. 2017 · 2017
Cited alongside, same era.
Optimization as a Model for Few-Shot Learning
Ravi, S.; and Larochelle, H. 2017 · 2017
Cited alongside, same era.
Joint optimization framework for learning with noisy labels
Tanaka, D.; Ikami, D.; Yamasaki, T.; and Aizawa, K. 2018 · 2018
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Learning to learn from noisy labeled data
Li, J.; Wong, Y.; Zhao, Q.; and Kankanhalli, M. S. 2019 · 2019
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DARTS: Differentiable Architecture Search
Liu, H.; Simonyan, K.; and Yang, Y. 2019 · 2019
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Are Anchor Points Really Indispensable in Label-Noise Learning?
Xia, X.; Liu, T.; Wang, N.; Han, B.; Gong, C.; Niu, G.; and Sugiyama, M. 2019 · 2019
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How does Disagreement Help Generalization against Label Corruption?
Yu, X.; Han, B.; Yao, J.; Niu, G.; Tsang, I.; and Sugiyama, M. 2019 · 2019
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Rethinking Importance Weighting for Deep Learning under Distribution Shift
Fang, T.; Lu, N.; Niu, G.; and Sugiyama, M. 2020 · 2020
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Part-dependent label noise: Towards instance-dependent label noise
Xia, X.; Liu, T.; Han, B.; Wang, N.; Gong, M.; Liu, H.; Niu, G.; Tao, D.; and Sugiyama, M. 2020 · 2020
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Dual T: Reducing estimation error for transition matrix in label-noise learning
Yao, Y.; Liu, T.; Han, B.; Gong, M.; Deng, J.; Niu, G.; and Sugiyama, M. 2020 · 2020
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Training convolutional networks with noisy labels
Sukhbaatar, S.; Bruna, J.; Paluri, M.; Bourdev, L.; and Fergus, R. 2014 · 2080
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