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The growing importance of massive datasets used for deep learning makesrobustness to label noise a critical property for classifiers to have. Sourcesof label noise include automatic labeling, non-expert labeling, and labelcorruption by data poisoning adversaries. Numerous previous works assume thatno source of labels can be trusted. We relax this assumption and assume that asmall subset of the training data is trusted. This enables substantial labelcorruption robustness performance gains. In addition, particularly severe labelnoise can be combated by using a set of trusted data with clean labels. Weutilize trusted data by proposing a loss correction technique that utilizestrusted examples in a data-efficient manner to mitigate the effects of labelnoise on deep neural network classifiers. Across vision and natural languageprocessing tasks, we experiment with various label noises at several strengths,and show that our method significantly outperforms existing methods.

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise