Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets. Although great progress has been made, existing techniques are limited in providing theoretical guarantees for the performance of the neural networks trained with noisy labels. Here we propose a novel approach with strong theoretical guarantees for robust training of deep networks trained with noisy labels. The key idea behind our method is to select weighted subsets (coresets) of clean data points that provide an approximately low-rank Jacobian matrix. We then prove that gradient descent applied to the subsets do not overfit the noisy labels. Our extensive experiments corroborate our theory and demonstrate that deep networks trained on our subsets achieve a significantly superior performance compared to state-of-the art, e.g., 6% increase in accuracy on CIFAR-10 with 80% noisy labels, and 7% increase in accuracy on mini Webvision.

提出了一种基于coresets和梯度下降的方法来处理具有噪声标签的深度神经网络的鲁棒性训练问题，并证明该方法不会过拟合噪声标签，实验证明该方法取得了诸如在CIFAR-10上以80％噪声标签训练后，准确度提高了6％，在mini Webvision上准确度提高了7％的显着优异表现。

针对噪声标签的神经网络鲁棒训练核心集