Boosting Co-teaching with Compression Regularization for Label Noise

04/28/2021 ∙ by Yingyi Chen, et al. ∙ 0

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 DivideMix. On ANIMAL-10N, we achieve 84.1 result by PLC is 83.4 strong baseline for learning with label noise. Our implementation is available at



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Code Repositories


(L2ID@CVPR2021) Boosting Co-teaching with Compression Regularization for Label Noise

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