To collect large scale annotated data, it is inevitable to introduce label noise, i.e., incorrect class labels. To be robust against label noise, many successful methods rely on the noisy classifiers (i.e., models trained on the noisy training data) to determine whether a label is trustworthy. However, it remains unknown why this heuristic works well in practice. In this paper, we provide the first theoretical explanation for these methods. We prove that the prediction of a noisy classifier can indeed be a good indicator of whether the label of a training data is clean. Based on the theoretical result, we propose a novel algorithm that corrects the labels based on the noisy classifier prediction. The corrected labels are consistent with the true Bayesian optimal classifier with high probability. We incorporate our label correction algorithm into the training of deep neural networks and train models that achieve superior testing performance on multiple public datasets.

本文介绍了针对大规模标注数据不可避免存在 label noise 问题时，通过使用 noisy classifiers 算法来提高模型鲁棒性，进而讲解了该算法的理论解释，并提出了一种基于该算法的标签校正方法，结合深度神经网络，成功提升了测试性能。

噪声标签的误差有界修正