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.

本研究针对标签噪声干扰下的图片分类模型，通过对Nested Dropout方法的再探索，发现这种方法可以对神经网络进行适当的矫正和压缩，进而应对标签噪声干扰，最终在两个现实世界数据集Clothing1M和ANIMAL-10N上取得与DivideMix和PLC相当或更好的性能结果，成为标签噪声干扰下学习的一个强大的基准模型。

压缩正则化强化共同教学以对抗标签噪声