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