The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
Open accessAbstract
In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject.
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Self-citationLearning From Noisy Labels With Deep Neural Networks: A Survey
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Symmetric Cross Entropy for Robust Learning With Noisy Labels
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CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
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Metadata
- Authors
- Lee, K.-H., He, X., Zhang, L., & Yang, L.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5447-5456
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962762068
- Cited by
- 478
- References
- 40
- FWCI
- 21.12
- Type
- conference paper
- Language
- en
- Open access
- green
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