  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. For example, on MNIST we obtain
test accuracy above 90 percent even after each clean training example has been
diluted with 100 randomly-labeled examples. Such behavior holds across multiple
patterns of label noise, even when erroneous labels are biased towards
confusing classes. We show that training in this regime requires a significant
but manageable increase in dataset size that is related to the factor by which
correct labels have been diluted. Finally, we provide an analysis of our
results that shows how increasing noise decreases the effective batch size.
