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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

6 Mar 2017NeurIPS 2017 12arXiv:1703.01780archive 2025-07-28

Antti Tarvainen, Harri Valpola

The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%.

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CuriousAI/mean-teacher officialmentioned in papermentioned on GitHubtfNOASSERTION report
INK-USC/DualRE mentioned on GitHubpytorch report
Lan1991Xu/ONE_NeurIPS2018 mentioned on GitHubpytorchMIT report
ZHKKKe/PixelSSL mentioned on GitHubpytorch report
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sud0301/semisup-semseg mentioned on GitHubpytorch report

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2ran · honoured contract
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accuracy sud0301/semisup-semseg/train_mlmt.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · fddaf151399b812a · report
cosine_loss sud0301/semisup-semseg/train_mlmt.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · f9bfc647f202a8e0 · report
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Tasks

Semi-Supervised Image ClassificationSemi-Supervised RGBD Semantic SegmentationSemi-Supervised Semantic SegmentationSource Free Object Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semi-Supervised Image Classification CIFAR-10, 250 Labels MeanTeacher Percentage error 47.32 #25 of 27 Archive leaderboard report
Semi-Supervised Image Classification CIFAR-10, 4000 Labels Mean Teacher Percentage error 6.28 #31 of 49 Archive leaderboard report
Semi-Supervised Image Classification ImageNet - 10% labeled data Mean Teacher (ResNeXt-152) Top 5 Accuracy 90.89% #51 of 75 Archive leaderboard report
Semi-Supervised Image Classification SVHN, 1000 labels Mean Teacher Accuracy 96.05 #13 of 17 Archive leaderboard report
Semi-Supervised Image Classification SVHN, 250 Labels MeanTeacher Accuracy 93.55 #11 of 15 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Voxel) mIoU (1% Labels) 41.0 #2 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Voxel) mIoU (10% Labels) 50.1 #2 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Voxel) mIoU (20% Labels) 52.8 #2 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Voxel) mIoU (50% Labels) 53.9 #2 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Range View) mIoU (1% Labels) 34.2 #7 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Range View) mIoU (10% Labels) 49.8 #7 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Range View) mIoU (20% Labels) 51.6 #7 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation ScribbleKITTI MeanTeacher (Range View) mIoU (50% Labels) 53.3 #7 of 9 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Voxel) mIoU (1% Labels) 45.4 #7 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Voxel) mIoU (10% Labels) 57.1 #7 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Voxel) mIoU (20% Labels) 59.2 #7 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Voxel) mIoU (50% Labels) 60.0 #7 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Range View) mIoU (1% Labels) 37.5 #10 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Range View) mIoU (10% Labels) 53.1 #10 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Range View) mIoU (20% Labels) 56.1 #10 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation SemanticKITTI MeanTeacher (Range View) mIoU (50% Labels) 57.4 #10 of 12 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Voxel) mIoU (1% Labels) 51.6 #4 of 11 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Voxel) mIoU (10% Labels) 66.0 #4 of 11 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Voxel) mIoU (20% Labels) 67.1 #4 of 11 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Voxel) mIoU (50% Labels) 71.7 #4 of 11 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Range View) mIoU (1% Labels) 42.1 #8 of 11 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Range View) mIoU (10% Labels) 60.4 #8 of 11 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Range View) mIoU (20% Labels) 65.4 #8 of 11 Archive leaderboard report
Semi-Supervised Semantic Segmentation nuScenes MeanTeacher (Range View) mIoU (50% Labels) 69.4 #8 of 11 Archive leaderboard report
Source Free Object Detection Cityscapes to Foggy Cityscapes MT AP50 34.3 #11 of 13 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

1x1 ConvolutionAverage PoolingBatch NormalizationConvolutionGlobal Average PoolingGrouped ConvolutionKaiming InitializationReLUResNeXtResNeXt BlockResidual Connection

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