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

1 code implementation • NeurIPS 2018 • Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel

We utilize trusted data by proposing a loss correction technique that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers.

Data Poisoning