The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Classification with Noisy Labels by Importance Reweighting
Open accessAbstract
In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability ρ ∈ [0,0.5), and the random label noise can be class-conditional. Here, we address two fundamental problems raised by this scenario. The first is how to best use the abundant surrogate loss functions designed for the traditional classification problem when there is label noise. We prove that any surrogate loss function can be used for classification with noisy labels by using importance reweighting, with consistency assurance that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. The other is the open problem of how to obtain the noise rate ρ. We show that the rate is upper bounded by the conditional probability P(∧Y|X) of the noisy sample. Consequently, the rate can be estimated, because the upper bound can be easily reached in classification problems. Experimental results on synthetic and real datasets confirm the efficiency of our methods.
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Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845
Learning From Noisy Labels With Deep Neural Networks: A Survey
Song, H., Kim, M., Park, D., Shin, Y., & Lee, J.-G. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3152527 · Cited by 1,308
Confident Learning: Estimating Uncertainty in Dataset Labels
Northcutt, C., Jiang, L., & Chuang, I. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12125 · Cited by 795
Learning from positive and unlabeled data: a survey
Bekker, J., & Davis, J. (2020) · Machine Learning · doi:10.1007/s10994-020-05877-5 · Cited by 651
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Ye, M., Zhang, X., Yuen, P. C., & Chang, S.-F. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00637 · Cited by 601
Metadata
- Authors
- Liu, T., & Tao, D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2016-03-01
- Volume
- 38
- Issue
- 3
- Pages
- 447-461
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W1514928307
- Cited by
- 782
- References
- 52
- FWCI
- 56.62
- Type
- journal article
- Language
- en
- Open access
- green
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