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In the presence of noisy or incorrect labels, neural networks have the undesirable tendency to memorize information about the noise.
Keeping the neural networks simple by minimizing the description length of the weights
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Classification in the presence of label noise: A survey
Frenay, B. and Verleysen, M · 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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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. E., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2014
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Training convolutional networks with noisy labels
Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L. D., and Fergus, R · 2014
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Adding gradient noise improves learning for very deep networks
Neelakantan, A., Vilnis, L., Le, Q. V., Sutskever, I., Kaiser, L., Kurach, K., and Martens, J · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 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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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
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A closer look at memorization in deep networks
Arpit, D., Jastrzebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al · 2017
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Dziugaite, G. and Roy, D · 2017
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Robust loss functions under label noise for deep neural networks
Ghosh, A., Kumar, H., and Sastry, P · 2017
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Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2017
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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
Cited alongside, same era.
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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Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
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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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Where is the information in a deep neural network?
Achille, A. and Soatto, S · 2019
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Unsupervised label noise modeling and loss correction
Arazo, E., Ortego, D., Albert, P., O’Connor, N. E., and McGuinness, K · 2019
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Understanding and utilizing deep neural networks trained with noisy labels
Chen, P., Liao, B., Chen, G., and Zhang, S · 2019
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Information-theoretic analysis of generalization capability of learning algorithms
Xu, A. and Raginsky, M · 2017
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Emergence of invariance and disentanglement in deep representations
Achille, A. and Soatto, S · 2018
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The description length of deep learning models
Blier, L. and Ollivier, Y · 2018
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.
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.
Measuring the intrinsic dimension of objective landscapes
Li, C., Farkhoor, H., Liu, R., and Yosinski, J · 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
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Deep self-learning from noisy labels
Han, J., Luo, P., and Wang, X · 2019
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Learning to learn from noisy labeled data
Li, J., Wong, Y., Zhao, Q., and Kankanhalli, M. S · 2019
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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
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L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Xu, Y., Cao, P., Kong, Y., and Wang, Y · 2019
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Safeguarded dynamic label regression for noisy supervision
Yao, J., Wu, H., Zhang, Y., Tsang, I., and Sun, J · 2019
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How does disagreement help generalization against label corruption?
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I. W.-H., and Sugiyama, M · 2019
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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.
Can gradient clipping mitigate label noise?
Menon, A. K., Rawat, A. S., Reddi, S. J., and Kumar, S · 2020
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Meta-learning without memorization
Yin, M., Tucker, G., Zhou, M., Levine, S., and Finn, C · 2020
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