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Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.
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Learning with noisy labels
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GloVe: Global vectors for word representation
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Training deep neural networks on noisy labels with bootstrapping
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Chainer: a next-generation open source framework for deep learning
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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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Empirical evaluation of rectified activations in convolutional network
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Deep learning
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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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Theoretical comparisons of positive-unlabeled learning against positive-negative learning
Niu, G., du Plessis, M. C., Sakai, T., Ma, Y., and Sugiyama, M · 2016
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Improved techniques for training gans
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A closer look at memorization in deep networks
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
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Using pre-training can improve model robustness and uncertainty
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S., Ishii, S., and Koyama, M · 2019
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Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
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Positive-unlabeled learning with non-negative risk estimator
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2017
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Making deep neural networks robust to label noise: a loss correction approach
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
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Suzuki, T · 2019
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Symmetric cross entropy for robust learning with noisy labels
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Are anchor points really indispensable in label-noise learning?
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Uncoupled regression from pairwise comparison data
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How does disagreement help generalization against label corruption?
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Confidence scores make instance-dependent label-noise learning possible
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Learning with bounded instance- and label-dependent label noise
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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
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Learning with multiple complementary labels
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Do we need zero training loss after achieving zero training error?
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Mitigating overfitting in supervised classification from two unlabeled datasets: A consistent risk correction approach
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Deep double descent: Where bigger models and more data hurt
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Parts-dependent label noise: Towards instance-dependent label noise
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Dual T: Reducing estimation error for transition matrix in label-noise learning
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