Papers › Meta Pseudo Labels
Meta Pseudo Labels
Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le
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. 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. Our code will be available at https://github.com/google-research/google-research/tree/master/meta_pseudo_labels.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | Meta Pseudo Labels (EfficientNet-L2) | Hardware Burden | 95040G | #6 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Meta Pseudo Labels (EfficientNet-L2) | Number of params | 480M | #6 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Meta Pseudo Labels (EfficientNet-L2) | Top 1 Accuracy | 90.2% | #6 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Meta Pseudo Labels (EfficientNet-L2) | Top 5 Accuracy | 98.8 | #6 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Meta Pseudo Labels (EfficientNet-B6-Wide) | Number of params | 390M | #10 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Meta Pseudo Labels (EfficientNet-B6-Wide) | Top 1 Accuracy | 90% | #10 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Meta Pseudo Labels (ResNet-50) | Top 1 Accuracy | 83.2% | #448 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | Meta Pseudo Labels (EfficientNet-B6-Wide) | Accuracy | 91.12% | #4 of 57 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | Meta Pseudo Labels (EfficientNet-L2) | Accuracy | 91.02% | #8 of 57 | Archive leaderboard | report |
| Semi-Supervised Image Classification | CIFAR-10, 4000 Labels | Meta Pseudo Labels (WRN-28-2) | Percentage error | 3.89± 0.07 | #5 of 49 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 10% labeled data | Meta Pseudo Labels (ResNet-50) | Top 1 Accuracy | 73.89% | #34 of 75 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 10% labeled data | Meta Pseudo Labels (ResNet-50) | Top 5 Accuracy | 91.38% | #34 of 75 | Archive leaderboard | report |
| Semi-Supervised Image Classification | SVHN, 1000 labels | Meta Pseudo Labels (WRN-28-2) | Accuracy | 98.01 ± 0.07 | #1 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Meta Pseudo Labels
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