Papers › Meta Pseudo Labels

Meta Pseudo Labels

23 Mar 2020CVPR 2021 1arXiv:2003.10580archive 2025-07-28

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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YanYan0716/MPL mentioned on GitHubtf report
kekmodel/MPL-pytorch mentioned on GitHubpytorch report
retoschiegg/meta-pseudo-labels mentioned on GitHubtf report
sayakpaul/PAWS-TF mentioned on GitHubtfApache-2.0 report
usccolumbia/tsdnn mentioned on GitHubpytorchMIT report
ve450su2021-group26/Algorithm mentioned on GitHubpytorch report

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2ran · our draft was wrong
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AutoContrast ifsheldon/MPL_Lightning/mpl_lightning/augmentation.py community (archive-listed) ran MIT (permissive) · c6e7c3451c85404f · report
Brightness ifsheldon/MPL_Lightning/mpl_lightning/augmentation.py community (archive-listed) ran MIT (permissive) · 0d1a8d6ce2015fc7 · report
Color ifsheldon/MPL_Lightning/mpl_lightning/augmentation.py community (archive-listed) ran MIT (permissive) · 32cb3f5760fa593e · report
class_eval usccolumbia/tsdnn/predict.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 3f0fe82035c4dbf7 · report
mae usccolumbia/tsdnn/predict.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 8d28ce13cc82e55f · report
add_data_specific_args ifsheldon/MPL_Lightning/mpl_lightning/datamodules.py community (archive-listed) unverified MIT (permissive) · cc7636585a82c554 · report
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collate_pool usccolumbia/tsdnn/tsdnn/data.py community (archive-listed) unverified MIT (permissive) · 3f6dd8d427414044 · report
get_cosine_schedule_with_warmup ifsheldon/MPL_Lightning/mpl_lightning/pl_modules.py community (archive-listed) unverified MIT (permissive) · ddf3bcf79dbb01ce · report
get_pos_unl_val_test_loader usccolumbia/tsdnn/tsdnn/data.py community (archive-listed) unverified MIT (permissive) · ae57990ad886dc45 · report
get_train_val_test_loader usccolumbia/tsdnn/pu_cgcnn/cgcnn/data.py community (archive-listed) unverified MIT (permissive) · 3b412198c15a2ba3 · report
validate usccolumbia/tsdnn/predict.py community (archive-listed) unverified MIT (permissive) · 431b44daf2725a69 · report
validate usccolumbia/tsdnn/pu_cgcnn/cgcnn/predict.py community (archive-listed) unverified MIT (permissive) · 8d0f8832124fc89d · report
x_u_split ifsheldon/MPL_Lightning/mpl_lightning/datamodules.py community (archive-listed) unverified MIT (permissive) · 78c331f531a14ce5 · report

Tasks

Image ClassificationMeta-LearningSemi-Supervised Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionDense ConnectionsDepthwise ConvolutionDepthwise Separable ConvolutionDropoutEfficientNetGlobal Average PoolingInverted Residual BlockKaiming InitializationMax PoolingMeta Pseudo LabelsPointwise ConvolutionRMSPropReLUResidual BlockResidual ConnectionSigmoid ActivationSqueeze-and-Excitation Block

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