Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at https://git.io/fjsvE

本文提出了一种使用beta混合模型作为无监督生成模型，实时估计样本被错误标记的概率的方法，并通过bootstrapping loss来校正模型。同时，作者还对mixup数据增强方法做了进一步优化，实验证明该方法具有比最近最先进的方法更强的标签噪声鲁棒性。

无监督标签噪声建模与损失修正