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.

翻译：神经网络因其强大的性能而得到广泛应用。然而，当训练数据存在标签噪声时，其性能会下降。受专家建议学习场景的启发——该场景中乘性权重更新最近被证明对专家建议中的适度数据损坏具有鲁棒性——我们提出在神经网络优化过程中使用乘性权重对样本进行重加权。我们从理论上证明了该方法与梯度下降结合使用时的收敛性，并在一维情况下证明了其优势。随后，我们通过在CIFAR-10、CIFAR-100和Clothing1M数据集上展示乘性权重在存在标签噪声时能提高神经网络的准确性，从经验上验证了我们在一般情况下的发现。我们还展示了该方法对对抗鲁棒性的影响。