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Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
TLDR
It is demonstrated that robustness to label noise up to severe strengths can be achieved by using a set of trusted data with clean labels, and a loss correction that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers is proposed.