  Neural networks are widespread due to their powerful performance. However,
they degrade in the presence of noisy labels at training time. Inspired by the
setting of learning with expert advice, where multiplicative weight (MW)
updates were recently shown to be robust to moderate data corruptions in expert
advice, we propose to use MW for reweighting examples during neural networks
optimization. We theoretically establish the convergence of our method when
used with gradient descent and prove its advantages in 1d cases. We then
validate our findings empirically for the general case by showing that MW
improves the accuracy of neural networks in the presence of label noise on
CIFAR-10, CIFAR-100 and Clothing1M. We also show the impact of our approach on
adversarial robustness.
