Papers › Boosting Co-teaching with Compression Regularization for Label Noise
Boosting Co-teaching with Compression Regularization for Label Noise
Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens
In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout. We find that Nested Dropout, though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching to further boost the performance. Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M and ANIMAL-10N. On Clothing1M, our approach obtains 74.9% accuracy which is slightly better than that of DivideMix. On ANIMAL-10N, we achieve 84.1% accuracy while the best public result by PLC is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://github.com/yingyichen-cyy/Nested-Co-teaching.
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Code
Syntology Ran 2 of 14 code samples harvested from 1 repository linked to this paper; 12 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | Clothing1M | NestedCoTeaching | Accuracy | 74.9% | #13 of 51 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | CE + Dropout | Accuracy | 81.3 | #17 of 19 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | CE + Dropout | ImageNet Pretrained | NO | #17 of 19 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | CE + Dropout | Network | Vgg19-BN | #17 of 19 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | Nested Dropout | Accuracy | 81.3 | #18 of 19 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | Nested Dropout | ImageNet Pretrained | NO | #18 of 19 | Archive leaderboard | report |
| Learning with noisy labels | ANIMAL | Nested Dropout | Network | Vgg19-BN | #18 of 19 | 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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