The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Learning from massive noisy labeled data for image classification
Abstract
Large-scale supervised datasets are crucial to train convolutional neural networks (CNNs) for various computer vision problems. However, obtaining a massive amount of well-labeled data is usually very expensive and time consuming. In this paper, we introduce a general framework to train CNNs with only a limited number of clean labels and millions of easily obtained noisy labels. We model the relationships between images, class labels and label noises with a probabilistic graphical model and further integrate it into an end-to-end deep learning system. To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels. Experiments on this dataset indicate that our approach can better correct the noisy labels and improves the performance of trained CNNs.
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Cites
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“Based on the observation of transferability of deep neural networks, people initialize parameters with a model pretrained on a larger yet related dataset [12], and then finetune on the smaller dataset of specific tasks [1,7,21].”
MethodLearning Hierarchical Features for Scene Labeling
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Cited by
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
Symmetric Cross Entropy for Robust Learning With Noisy Labels
Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., & Bailey, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00041 · Cited by 1,046
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Karimi, D., Dou, H., Warfield, S. K., & Gholipour, A. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101759 · Cited by 706
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
Lee, K.-H., He, X., Zhang, L., & Yang, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00571 · Cited by 478
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
Guo, S., Huang, W., Zhang, H., Zhuang, C., Dong, D., Scott, M. R., & Huang, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_9 · Cited by 319
Metadata
- Authors
- Xiao, T., Xia, T., Yang, Y., Huang, C., & Wang, X.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 2691-2699
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1921293667
- Cited by
- 907
- References
- 31
- FWCI
- 20.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xiao, T., Xia, T., Yang, Y., Huang, C., & Wang, X. (2015). Learning from massive noisy labeled data for image classification. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298885
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