Coresets for Robust Training of Neural Networks against Noisy Labels
This work addresses the challenge of robust training for deep networks in real-world datasets with noisy labels, offering a novel method with strong theoretical guarantees.
The paper tackles the problem of neural networks overfitting noisy labels by proposing a coreset-based approach that selects weighted subsets of clean data points to ensure an approximately low-rank Jacobian matrix, resulting in significant performance improvements such as a 6% accuracy increase on CIFAR-10 with 80% noisy labels and a 7% increase on mini Webvision.
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.