  Collecting large-scale data with clean labels for supervised training of
neural networks is practically challenging. Although noisy labels are usually
cheap to acquire, existing methods suffer a lot from label noise. This paper
targets at the challenge of robust training at high label noise regimes. The
key insight to achieve this goal is to wisely leverage a small trusted set to
estimate exemplar weights and pseudo labels for noisy data in order to reuse
them for supervised training. We present a holistic framework to train deep
neural networks in a way that is highly invulnerable to label noise. Our method
sets the new state of the art on various types of label noise and achieves
excellent performance on large-scale datasets with real-world label noise. For
instance, on CIFAR100 with a $40\%$ uniform noise ratio and only 10 trusted
labeled data per class, our method achieves $80.2{\pm}0.3\%$ classification
accuracy, where the error rate is only $1.4\%$ higher than a neural network
trained without label noise. Moreover, increasing the noise ratio to $80\%$,
our method still maintains a high accuracy of $75.5{\pm}0.2\%$, compared to the
previous best accuracy $48.2\%$.
  Source code available:
https://github.com/google-research/google-research/tree/master/ieg
