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Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption.
Nonlinear programming
Kuhn, H., Tucker, A., et al · 1951
Earlier work this paper cites.
Conjugate priors for exponential families
Diaconis, P. and Ylvisaker, D · 1979
Earlier work this paper cites.
Learning from noisy examples
Angluin, D. and Laird, P · 1988
Earlier work this paper cites.
Support-vector networks
Cortes, C. and Vapnik, V · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition, 1998
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
On contraction properties of markov kernels
Del Moral, P., Ledoux, M., and Miclo, L · 2003
Earlier work this paper cites.
Learning object categories from Google’s image search
Fergus, R., Fei-Fei, L., Perona, P., and Zisserman, A · 2005
Earlier work this paper cites.
Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D · 2006
Earlier work this paper cites.
On the consistency of multiclass classification methods
Tewari, A. and Bartlett, P. L · 2007
Earlier work this paper cites.
Efficient and accurate ℓ p \ell_{p} -norm multiple kernel learning
Kloft, M., Brefeld, U., Sonnenburg, S., Laskov, P., Müller, K., and Zien, A · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Krizhevsky, A · 2009
Earlier work this paper cites.
On the foundations of noise-free selective classification
El-Yaniv, R. and Wiener, Y · 2010
Earlier work this paper cites.
Random classification noise defeats all convex potential boosters
Long, P. M. and Servedio, R. A · 2010
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.
Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., and Handy, G · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G · 2013
Earlier work this paper cites.
Decontamination of mutually contaminated models
Blanchard, G. and Scott, C · 2014
Earlier work this paper cites.
Analysis of learning from positive and unlabeled data
du Plessis, M. C., Niu, G., and Sugiyama, M · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2015
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.
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
Earlier work this paper cites.
Learning with symmetric label noise: The importance of being unhinged
Van Rooyen, B., Menon, A., and Williamson, R. 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.
Deep learning
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Mixture proportion estimation via kernel embeddings of distributions
Ramaswamy, H., Scott, C., and Tewari, A · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al · 2017
Cited alongside, same era.
Robust loss functions under label noise for deep neural networks
Ghosh, A., Kumar, H., and Sastry, P · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2017
Cited alongside, same era.
Combating label noise in deep learning using abstention
Thulasidasan, S., Bhattacharya, T., Bilmes, J., Chennupati, G., and Mohd-Yusof, J · 2019
Later among the works it cites.
Symmetric cross entropy for robust learning with noisy labels
Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., and Bailey, J · 2019
Later among the works it cites.
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
Later among the works it cites.
How does disagreement help generalization against label corruption?
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., and Sugiyama, M · 2019
Later among the works it cites.
Learning with bounded instance-and label-dependent label noise
Cheng, J., Liu, T., Ramamohanarao, K., and Tao, D · 2020
Later among the works it cites.
Can cross entropy loss be robust to label noise?
Feng, L., Shu, S., Lin, Z., Lv, F., Li, L., and An, B · 2020
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
beta-VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Learning from complementary labels
Ishida, T., Niu, G., Hu, W., and Sugiyama, M · 2017
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Decoupling “when to update” from “how to update”
Malach, E. and Shalev-Shwartz, S · 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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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
Cited alongside, same era.
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Beyond synthetic noise: Deep learning on controlled noisy labels
Jiang, L., Huang, D., Liu, M., and Yang, W · 2020
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DivideMix: Learning with noisy labels as semi-supervised learning
Li, J., Socher, R., and Hoi, S. C · 2020
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Peer loss functions: Learning from noisy labels without knowing noise rates
Liu, Y. and Guo, H · 2020
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Curriculum loss: Robust learning and generalization against label corruption
Lyu, Y. and Tsang, I. W · 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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Coresets for robust training of deep neural networks against noisy labels
Mirzasoleiman, B., Cao, K., and Leskovec, J · 2020
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Consistent estimators for learning to defer to an expert
Mozannar, H. and Sontag, D · 2020
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SELF: Learning to filter noisy labels with self-ensembling
Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., and Brox, T · 2020
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Intra order-preserving functions for calibration of multi-class neural networks
Rahimi, A., Shaban, A., Cheng, C.-A., Hartley, R., and Boots, B · 2020
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Combating noisy labels by agreement: A joint training method with co-regularization
Wei, H., Feng, L., Chen, X., and An, B · 2020
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A topological filter for learning with label noise
Wu, P., Zheng, S., Goswami, M., Metaxas, D., and Chen, C · 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
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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
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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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Classification with rejection based on cost-sensitive classification
Charoenphakdee, N., Cui, Z., Zhang, Y., and Sugiyama, M · 2021
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Robustness of accuracy metric and its inspirations in learning with noisy labels
Chen, P., Ye, J., Chen, G., Zhao, J., and Heng, P.-A · 2021
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Provably end-to-end label-noise learning without anchor point
Li, X., Liu, T., Han, B., Niu, G., and Sugiyama, M · 2021
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