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Instance- and Label-dependent label Noise (ILN) widely exists in real-world datasets but has been rarely studied.
Learning from noisy examples
Angluin, D. and Laird, P · 1988
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Learning linear threshold functions in the presence of classification noise
Bylander, T · 1994
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A database for handwritten text recognition research
Hull, J. J · 1994
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Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
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Efficient noise-tolerant learning from statistical queries
Kearns, M. J · 1998
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Identifying mislabeled training data
Brodley, C. E. and Friedl, M. A · 1999
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Estimating a kernel fisher discriminant in the presence of label noise
Lawrence, N. D. and Scholkopf, B · 2001
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Support vector machine active learning with applications to text classification
Tong, S. and Koller, D · 2001
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Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, P. L. and Mendelson, S · 2002
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A new boosting algorithm using input-dependent regularizer
Jin, R., Liu, Y., Si, L., Carbonell, J. G., and Hauptmann, A. G · 2003
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Eliminating class noise in large datasets
Zhu, X., Wu, X., and Chen, Q · 2003
Earlier work this paper cites.
Pruning training sets for learning of object categories
Angelova, A., Abu-Mostafam, Y., and Perona, P · 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.
Risk bounds for statistical learning
Massart, P. and Nédélec, É · 2006
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
Huang, J., Gretton, A., Borgwardt, K. M., Schölkopf, B., and Smola, A. J · 2007
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Noise tolerant variants of the perceptron algorithm
Khardon, R. and Wachman, G · 2007
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Learning object categories from internet image searches
Fergus, R., Fei-Fei, L., Perona, P., and Zisserman, A · 2010
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Random classification noise defeats all convex potential boosters
Long, P. M. and Servedio, R. A · 2010
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Active learning literature survey
Settles, B · 2010
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Harvesting image databases from the web
Schroff, F., Criminisi, A., and Zisserman, A · 2011
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Label-noise robust logistic regression and its applications
Bootkrajang, J. and Kaban, A · 2012
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Calibrated asymmetric surrogate losses
Scott, C. et al · 2012
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Boosting in the presence of label noise
Bootkrajang, J. and Kaban, A · 2013
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Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
Earlier work this paper cites.
Classification in the presence of label noise: A survey
Frénay, B. and Verleysen, M · 2014
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Efficient learning of linear separators under bounded noise
Awasthi, P., Balcan, M.-F., Haghtalab, N., and Urner, R · 2015
Earlier work this paper cites.
Making risk minimization tolerant to label noise
Ghosh, A., Manwani, N., and Sastry, P · 2015
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Learning from corrupted binary labels via class-probability estimation
Menon, A. K., Rooyen, B. V., Ong, C. S., and Williamson, R. C · 2015
Cited alongside, same era.
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
Cited alongside, same era.
Learning with symmetric label noise: The importance of being unhinged
van Rooyen, B., Menon, A., and Williamson, R. C · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
Cited alongside, same era.
Learning and 1-bit compressed sensing under asymmetric noise
Awasthi, P., Balcan, M.-F., Haghtalab, N., and Zhang, H · 2016
Cited alongside, same era.
On the consistency of exact and approximate nearest neighbor with noisy data
Iterative learning with open-set noisy labels
Wang, Y., Liu, W., Ma, X., Bailey, J., Zha, H., Song, L., and Xia, S.-T · 2018
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Learning with biased complementary labels
Yu, X., Liu, T., Gong, M., and Tao, D · 2018
Closest in time.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M. R · 2018
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Distribution-independent pac learning of halfspaces with massart noise
Diakonikolas, I., Gouleakis, T., and Tzamos, C · 2019
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Deep self-learning from noisy labels
Han, J., Luo, P., and Wang, X · 2019
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O2u-net: A simple noisy label detection approach for deep neural networks
Huang, J., Qu, L., Jia, R., and Zhao, B · 2019
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Gao, W., Niu, X., and Zhou, Z · 2016
Cited alongside, same era.
Feasibility of active machine learning for multiclass compound classification
Lang, T., Flachsenberg, F., von Luxburg, U., and Rarey, M · 2016
Cited alongside, same era.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 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.
Mixture proportion estimation via kernel embeddings of distributions
Ramaswamy, H., Scott, C., and Tewari, A · 2016
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.
Closest in time.
Nlnl: Negative learning for noisy labels
Kim, Y., Yim, J., Yun, J., and Kim, J · 2019
Closest in time.
Learning to learn from noisy labeled data
Li, J., Wong, Y., Zhao, Q., and Kankanhalli, M. S · 2019
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Combinatorial inference against label noise
Seo, P. H., Kim, G., and Han, B · 2019
Closest in time.
Symmetric cross entropy for robust learning with noisy labels
Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., and Bailey, J · 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
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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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Probabilistic end-to-end noise correction for learning with noisy labels
Yi, K. and Wu, J · 2019
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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
Feng, L., Kaneko, T., Han, B., Niu, G., An, B., and Sugiyama, M · 2020
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Sigua: Forgetting may make learning with noisy labels more robust
Han, B., Niu, G., Yu, X., Yao, Q., Xu, M., Tsang, I., and Sugiyama, M · 2020
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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
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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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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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Class2simi: A new perspective on learning with label noise
Wu, S., Xia, X., Liu, T., Han, B., Gong, M., Wang, N., Liu, H., and Niu, G · 2020
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Parts-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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Generative-discriminative complementary learning
Xu, Y., Gong, M., Chen, J., Liu, T., Zhang, K., and Batmanghelich, K · 2020
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Searching to exploit memorization effect in learning from noisy labels
Yao, Q., Yang, H., Han, B., Niu, G., and Kwok, J · 2020
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Label-noise robust domain adaptation
Yu, X., Liu, T., Gong, M., Zhang, K., Batmanghelich, K., and Tao, D · 2020
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Error-bounded correction of noisy labels
Zheng, S., Wu, P., Goswami, A., Goswami, M., Metaxas, D., and Chen, C · 2020
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