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In the presence of noisy or incorrect labels, neural networks have theundesirable tendency to memorize information about the noise. Standardregularization techniques such as dropout, weight decay or data augmentationsometimes help, but do not prevent this behavior. If one considers neuralnetwork weights as random variables that depend on the data and stochasticityof training, the amount of memorized information can be quantified with theShannon mutual information between weights and the vector of all traininglabels given inputs, $I(w ; \mathbf{y} \mid \mathbf{x})$. We show that for anytraining algorithm, low values of this term correspond to reduction inmemorization of label-noise and better generalization bounds. To obtain theselow values, we propose training algorithms that employ an auxiliary networkthat predicts gradients in the final layers of a classifier without accessinglabels. We illustrate the effectiveness of our approach on versions of MNIST,CIFAR-10, and CIFAR-100 corrupted with various noise models, and on alarge-scale dataset Clothing1M that has noisy labels.

Improving Generalization by Controlling Label-Noise Information in Neural Network Weights