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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Citations
Cites
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Cited by
MLP-Mixer: An all-MLP Architecture for Vision
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Meta-Learning in Neural Networks: A Survey
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Meta-Learning in Neural Networks: A Survey
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Extract Free Dense Labels from CLIP
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“During this work, the current research with the best impact on the mentioned dataset is the Meta Pseudo Labels approach [41].”
MethodCompute Trends Across Three Eras of Machine Learning
Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891914 · Cited by 363
A Survey on Semi-, Self- and Unsupervised Learning for Image Classification
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MultiMAE: Multi-modal Multi-task Masked Autoencoders
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Li, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147
Face2Exp: Combating Data Biases for Facial Expression Recognition
Zeng, D., Lin, Z., Yan, X., Liu, Y., Wang, F., & Tang, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01965 · Cited by 141
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Srinidhi, C. L., Kim, S. W., Chen, F.-D., & Martel, A. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102256 · Cited by 116
Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation
Ma, Y., Wang, J., Yang, J., & Wang, L. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3348982 · Cited by 55
AI and Compute: How Much Longer Can Computing Power Drive Artificial Intelligence Progress?
Lohn, A., & Musser, M. (2022) · Center for Security and Emerging Technology · doi:10.51593/2021ca009 · Cited by 39
Losses over Labels: Weakly Supervised Learning via Direct Loss Construction
Sam, D., & Kolter, J. Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26159 · Cited by 6
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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