  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
