Papers › All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
Islam Nassar, Samitha Herath, Ehsan Abbasnejad, Wray Buntine, Gholamreza Haffari
Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model's prediction to make labeling decisions without considering any prior knowledge about the visual similarity among the classes. In this paper, we demonstrate that this degrades the quality of pseudo-labeling as it poorly represents visually similar classes in the pool of pseudo-labeled data. We propose SemCo, a method which leverages label semantics and co-training to address this problem. We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together. We then co-train the two classifiers to learn based on their disagreements. We show that our method achieves state-of-the-art performance across various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet dataset with 1000 labeled examples. We also show that our method requires smaller batch size and fewer training iterations to reach its best performance. We make our code available at https://github.com/islam-nassar/semco.
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Code
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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 | CIFAR-10, 4000 Labels | SemCo (μ=7) | Percentage error | 3.8±0.08 | #4 of 49 | Archive leaderboard | report |
| Semi-Supervised Image Classification | Mini-ImageNet, 1000 Labels | SemCo (μ=3) | Accuracy | 44.65±0.71 | #2 of 3 | Archive leaderboard | report |
| Semi-Supervised Image Classification | Mini-ImageNet, 1000 Labels | SemCo (μ=7) | Accuracy | 40.65±0.23 | #3 of 3 | Archive leaderboard | report |
| Semi-Supervised Image Classification | Mini-ImageNet, 10000 Labels | SemCo (μ=3) | Accuracy | 58.75±0.76 | #2 of 3 | Archive leaderboard | report |
| Semi-Supervised Image Classification | Mini-ImageNet, 10000 Labels | SemCo (μ=7) | Accuracy | 57.22±0.35 | #3 of 3 | Archive leaderboard | report |
| Semi-Supervised Image Classification | Mini-ImageNet, 4000 Labels | SemCo (μ=3) | Accuracy | 53.99±0.93 | #3 of 4 | Archive leaderboard | report |
| Semi-Supervised Image Classification | Mini-ImageNet, 4000 Labels | SemCo (μ=7) | Accuracy | 50.54±2.20 | #4 of 4 | Archive leaderboard | report |
| Semi-Supervised Image Classification | cifar-100, 10000 Labels | SemCo (μ=7) | Percentage error | 24.45±0.12 | #21 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.
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