The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Meta Pseudo Labels
Abstract
We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing state-of-the-art [16]. Like Pseudo Labels, Meta Pseudo Labels has a teacher network to generate pseudo labels on unlabeled data to teach a student network. However, unlike Pseudo Labels where the teacher is fixed, the teacher in Meta Pseudo Labels is constantly adapted by the feedback of the student’s performance on the labeled dataset. As a result, the teacher generates better pseudo labels to teach the student.1
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Cites
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Cited by
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Meta-Learning in Neural Networks: A Survey
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Metadata
- Authors
- Pham, H., Dai, Z., Xie, Q., & Le, Q. V.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 11552-11563
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3180562345
- Cited by
- 509
- References
- 23
- FWCI
- 47.76
- Type
- conference paper
- Language
- en
- Open access
- closed
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