Papers › Milking CowMask for Semi-Supervised Image Classification
Milking CowMask for Semi-Supervised Image Classification
Geoff French, Avital Oliver, Tim Salimans
Consistency regularization is a technique for semi-supervised learning that underlies a number of strong results for classification with few labeled data. It works by encouraging a learned model to be robust to perturbations on unlabeled data. Here, we present a novel mask-based augmentation method called CowMask. Using it to provide perturbations for semi-supervised consistency regularization, we achieve a state-of-the-art result on ImageNet with 10% labeled data, with a top-5 error of 8.76% and top-1 error of 26.06%. Moreover, we do so with a method that is much simpler than many alternatives. We further investigate the behavior of CowMask for semi-supervised learning by running many smaller scale experiments on the SVHN, CIFAR-10 and CIFAR-100 data sets, where we achieve results competitive with the state of the art, indicating that CowMask is widely applicable. We open source our code at https://github.com/google-research/google-research/tree/master/milking_cowmask
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Image Classification | ImageNet - 10% labeled data | CowMix (ResNet-152) | Top 1 Accuracy | 73.94% | #32 of 75 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 10% labeled data | CowMix (ResNet-152) | Top 5 Accuracy | 91.24% | #32 of 75 | Archive leaderboard | report |
| Semi-Supervised Image Classification | cifar-100, 10000 Labels | CowMix (WRN-28-96x2d) | Percentage error | 23.07±0.30 | #18 of 29 | 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
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