Papers › SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
Khanh-Binh Nguyen
Semi-supervised learning (SSL) has become popular in recent years because it allows the training of a model using a large amount of unlabeled data. However, one issue that many SSL methods face is the confirmation bias, which occurs when the model is overfitted to the small labeled training dataset and produces overconfident, incorrect predictions. To address this issue, we propose SequenceMatch, an efficient SSL method that utilizes multiple data augmentations. The key element of SequenceMatch is the inclusion of a medium augmentation for unlabeled data. By taking advantage of different augmentations and the consistency constraints between each pair of augmented examples, SequenceMatch helps reduce the divergence between the prediction distribution of the model for weakly and strongly augmented examples. In addition, SequenceMatch defines two different consistency constraints for high and low-confidence predictions. As a result, SequenceMatch is more data-efficient than ReMixMatch, and more time-efficient than both ReMixMatch (×4) and CoMatch (×2) while having higher accuracy. Despite its simplicity, SequenceMatch consistently outperforms prior methods on standard benchmarks, such as CIFAR-10/100, SVHN, and STL-10. It also surpasses prior state-of-the-art methods by a large margin on large-scale datasets such as ImageNet, with a 38.46% error rate. Code is available at https://github.com/beandkay/SequenceMatch.
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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 | SequenceMatch (ResNet-50) | Top 1 Accuracy | 75.2% | #28 of 75 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 10% labeled data | SequenceMatch (ResNet-50) | Top 5 Accuracy | 91.9 | #28 of 75 | 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.
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