Fetching the paper…
Reading the bibliography…
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization of deep neural networks (DNNs).
Sub-gaussian random variables
Buldygin, V. V. and Kozachenko, Y. V · 1980
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Iterative learning for reliable crowdsourcing systems
Karger, D., Oh, S., and Shah, D · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Variational inference for crowdsourcing
Liu, Q., Peng, J., and Ihler, A · 2012
Earlier work this paper cites.
Extended T: Learning with mixed closed-set and open-set noisy labels
Xia, X., Liu, T., Han, B., Wang, N., Deng, J., Li, J., and Mao, Y · 2012
Earlier work this paper cites.
Efficient crowdsourcing for multi-class labeling
Karger, D. R., Oh, S., and Shah, D · 2013
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.
Spectral methods meet em: A provably optimal algorithm for crowdsourcing
Zhang, Y., Chen, X., Zhou, D., and Jordan, M. I · 2014
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2015
Earlier work this paper cites.
An online learning approach to improving the quality of crowd-sourcing
Liu, Y. and Liu, M · 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.
Learning statistical models of phenotypes using noisy labeled training data
Agarwal, V., Podchiyska, T., Banda, J. M., Goel, V., Leung, T. I., Minty, E. P., Sweeney, T. E., Gyang, E., and Shah, N. H · 2016
Earlier work this paper cites.
On the resistance of nearest neighbor to random noisy labels
Gao, W., Yang, B.-B., and Zhou, Z.-H · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
Earlier work this paper cites.
Robust loss functions under label noise for deep neural networks
Ghosh, A., Kumar, H., and Sastry, P · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Improving crowdsourced label quality using noise correction
Zhang, J., Sheng, V. S., Li, T., and Wu, X · 2017
Earlier work this paper cites.
Decomposition-based evolutionary multiobjective optimization to self-paced learning
Gong, M., Li, H., Meng, D., Miao, Q., and Liu, J · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
To trust or not to trust a classifier
Jiang, H., Kim, B., Guan, M., and Gupta, M · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
Cited alongside, same era.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
Cited alongside, same era.
Robust bi-tempered logistic loss based on bregman divergences
Amid, E., Warmuth, M. K., Anil, R., and Koren, T · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Estimating training data influence by tracing gradient descent
Pruthi, G., Liu, F., Kale, S., and Sundararajan, M · 2020
Later among the works it cites.
Learning adaptive loss for robust learning with noisy labels
Shu, J., Zhao, Q., Chen, K., Xu, Z., and Meng, D · 2020
Later among the works it cites.
Seminll: A framework of noisy-label learning by semi-supervised learning
Wang, Z., Jiang, J., Han, B., Feng, L., An, B., Niu, G., and Long, G · 2020
Later among the works it cites.
Combating noisy labels by agreement: A joint training method with co-regularization
Wei, H., Feng, L., Chen, X., and An, B · 2020
Later among the works it cites.
When optimizing f f -divergence is robust with label noise
Wei, J. and Liu, Y · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
O2u-net: A simple noisy label detection approach for deep neural networks
Huang, J., Qu, L., Jia, R., and Zhao, B · 2019
Cited alongside, same era.
Invariant information clustering for unsupervised image classification and segmentation
Ji, X., Henriques, J. F., and Vedaldi, A · 2019
Cited alongside, same era.
Fast rates for a knn classifier robust to unknown asymmetric label noise
Reeve, H. and Kabán, A · 2019
Cited alongside, same era.
Symmetric cross entropy for robust learning with noisy labels
Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., and Bailey, J · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Unsupervised data augmentation
Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., and Le, Q. V · 2019
Cited alongside, same era.
Searching to exploit memorization effect in learning with noisy labels
Yao, Q., Yang, H., Han, B., Niu, G., and Kwok, J. T · 2020
Later among the works it cites.
Me-momentum: Extracting hard confident examples from noisily labeled data
Bai, Y. and Liu, T · 2021
Closest in time.
Understanding and improving early stopping for learning with noisy labels
Bai, Y., Yang, E., Han, B., Yang, Y., Li, J., Mao, Y., Niu, G., and Liu, T · 2021
Closest in time.
Can cross entropy loss be robust to label noise?
Feng, L., Shu, S., Lin, Z., Lv, F., Li, L., and An, B · 2021
Closest in time.
Contrastive learning improves model robustness under label noise
Ghosh, A. and Lan, A · 2021
Closest in time.
A survey on contrastive self-supervised learning
Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., and Makedon, F · 2021
Closest in time.
Understanding instance-level label noise: Disparate impacts and treatments
Liu, Y · 2021
Closest in time.
Can less be more? when increasing-to-balancing label noise rates considered beneficial
Liu, Y. and Wang, J · 2021
Closest in time.
An empirical study and analysis on open-set semi-supervised learning
Luo, H., Cheng, H., Meng, F., Gao, Y., Li, K., Zhang, M., and Sun, X · 2021
Closest in time.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Closest in time.
Fair classification with group-dependent label noise
Wang, J., Liu, Y., and Levy, C · 2021
Closest in time.
Robust early-learning: Hindering the memorization of noisy labels
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., and Chang, Y · 2021
Closest in time.
Learning from noisy labels with no change to the training process
Zhang, M., Lee, J., and Agarwal, S · 2021
Closest in time.
Federated bandit: A gossiping approach
Zhu, Z., Zhu, J., Liu, J., and Liu, Y · 2021
Closest in time.
An information fusion approach to learning with instance-dependent label noise
Jiang, Z., Zhou, K., Liu, Z., Li, L., Chen, R., Choi, S.-H., and Hu, X · 2022
Closest in time.
Identifiability of label noise transition matrix
Liu, Y · 2022
Closest in time.