强力神经网络网络优化的多倍比重 (Multiplicative Reweighting for Robust Neural Network Optimization)

Yet, their performance degrades in the presence of noisy labels at train time. Inspired by the setting of learning with expert advice, where multiplicative weights (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 advantage for label noise in 1d cases. We then validate empirically our findings for the general case by showing that MW improves neural networks accuracy 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：然而,他们的性能却在火车上出现吵闹的标签,在专家建议下进行的学习的启发下,最近发现多倍重(MW)的更新对缓和专家建议中的数据腐败十分有力,我们提议在神经网络优化时使用MW作为重量例子,在理论上我们的方法在使用梯度下降时会趋于一致,并在1D案例中证明它有利于标签噪音。然后,我们用经验验证了我们关于一般案例的调查结果,表明MW在CFAR-10、CIFAR-100和Stragl1M的标签噪音面前提高了神经网络的准确性。我们还展示了我们的方法对对抗性强力的影响。