Deep Learning is Robust to Massive Label Noise

Understand

Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks.

• However, well-annotated datasets can be time-consuming and expensive to collect, lending increased interest to larger but noisy datasets that are more easily obtained.
• In this paper, we show that deep neural networks are capable of generalizing from training data for which true labels are massively outnumbered by incorrect labels.
• We demonstrate remarkably high test performance after training on corrupted data from MNIST, CIFAR, and ImageNet.