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Collecting large-scale data with clean labels for supervised training ofneural networks is practically challenging. Although noisy labels are usuallycheap to acquire, existing methods suffer a lot from label noise. This papertargets at the challenge of robust training at high label noise regimes. Thekey insight to achieve this goal is to wisely leverage a small trusted set toestimate exemplar weights and pseudo labels for noisy data in order to reusethem for supervised training. We present a holistic framework to train deepneural networks in a way that is highly invulnerable to label noise. Our methodsets the new state of the art on various types of label noise and achievesexcellent performance on large-scale datasets with real-world label noise. Forinstance, on CIFAR100 with a $40\%$ uniform noise ratio and only 10 trustedlabeled data per class, our method achieves $80.2{\pm}0.3\%$ classificationaccuracy, where the error rate is only $1.4\%$ higher than a neural networktrained 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 theprevious best accuracy $48.2\%$.  Source code available:https://github.com/google-research/google-research/tree/master/ieg

Distilling Effective Supervision from Severe Label Noise