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In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model.
Learning from noisy examples
Angluin, D. and Laird, P · 1988
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Kearns, M · 1993
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H · 2000
Earlier work this paper cites.
Statistical behavior and consistency of classification methods based on convex risk minimization
Zhang, T. et al · 2004
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.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
Earlier work this paper cites.
Covariate shift adaptation by importance weighted cross validation
Sugiyama, M., Krauledat, M., and Müller, K.-R · 2007
Earlier work this paper cites.
Cheap and fast – but is it good? evaluating non-expert annotations for natural language tasks
Snow, R., O’Connor, B., Jurafsky, D., and Ng, A · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. et al · 2009
Earlier work this paper cites.
On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost
Masnadi-shirazi, H. and Vasconcelos, N · 2009
Earlier work this paper cites.
On the efficient minimization of classification calibrated surrogates
Nock, R. and Nielsen, F · 2009
Earlier work this paper cites.
Supervised learning from multiple experts: whom to trust when everyone lies a bit
Raykar, V. C., Yu, S., Zhao, L. H., Jerebko, A., Florin, C., Valadez, G. H., Bogoni, L., and Moy, L · 2009
Earlier work this paper cites.
Learning SVMs from sloppily labeled data
Stempfel, G. and Ralaivola, L · 2009
Earlier work this paper cites.
Learning with instance-dependent label noise: A sample sieve approach
Cheng, H., Zhu, Z., Li, X., Gong, Y., Sun, X., and Liu, Y · 2010
Earlier work this paper cites.
Composite binary losses
Reid, M. D. and Williamson, R. C · 2010
Earlier work this paper cites.
Modeling annotator expertise: Learning when everybody knows a bit of something
Yan, Y., Rosales, R., Fung, G., Schmidt, M., Hermosillo, G., Bogoni, L., Moy, L., and Dy, J · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Earlier work this paper cites.
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Sugiyama, M. and Kawanabe, M · 2012
Earlier work this paper cites.
Noise tolerance under risk minimization
Manwani, N. and Sastry, P. S · 2013
Earlier work this paper cites.
Learning with Noisy Labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
Cited alongside, same era.
Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., and Handy, G · 2013
Cited alongside, same era.
Making risk minimization tolerant to label noise
Ghosh, A., Manwani, N., and S. Sastry, P · 2014
Cited alongside, same era.
Modelling class noise with symmetric and asymmetric distributions
Du, J. and Cai, Z · 2015
Cited alongside, same era.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2015
Cited alongside, same era.
Learning from corrupted binary labels via class-probability estimation
Menon, A., Van Rooyen, B., Ong, C. S., and Williamson, B · 2015
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
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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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Towards instance-dependent label noise-tolerant classification: a probabilistic approach
Bootkrajang, J. and Chaijaruwanich, J · 2018
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Who said what: Modeling individual labelers improves classification
Guan, M. Y., Gulshan, V., Dai, A. M., and Hinton, G. E · 2018
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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
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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 · 2015
Cited alongside, same era.
Training convolutional networks with noisy labels
Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., and Fergus, R · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Cited alongside, same era.
Learning in the Presence of Corruption
van Rooyen, B. and Williamson, R. C · 2015
Cited alongside, same era.
Learning from binary labels with instance-dependent corruption
Menon, A. K., Van Rooyen, B., and Natarajan, N · 2016
Cited alongside, same era.
Loss factorization, weakly supervised learning and label noise robustness
Patrini, G., Nielsen, F., Nock, R., and Carioni, M · 2016
Cited alongside, same era.
Ishida, T., Niu, G., and Sugiyama, M · 2018
Later among the works it cites.
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 · 2018
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Learning from noisy singly-labeled data
Khetan, A., Lipton, Z. C., and Anandkumar, A · 2018
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Dimensionality-driven learning with noisy labels
Ma, X., Wang, Y., Houle, M. E., Zhou, S., Erfani, S., Xia, S., Wijewickrema, S., and Bailey, J · 2018
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Learning from binary labels with instance-dependent noise
Menon, A. K., van Rooyen, B., and Natarajan, N · 2018
Later among the works it cites.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2018
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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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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 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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Adversarial label learning
Arachie, C. and Huang, B · 2019
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On Symmetric Losses for Learning from Corrupted Labels
Charoenphakdee, N., Lee, J., and Sugiyama, M · 2019
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A generalized neyman-pearson criterion for optimal domain adaptation
Scott, C · 2019
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Learning with bad training data via iterative trimmed loss minimization
Shen, Y. and Sanghavi, S · 2019
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Snorkel: Rapid training data creation with weak supervision
Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., and Ré, C · 2020
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