  Noisy labels are ubiquitous in real-world datasets, which poses a challenge
for robustly training deep neural networks (DNNs) as DNNs usually have the high
capacity to memorize the noisy labels. In this paper, we find that the test
accuracy can be quantitatively characterized in terms of the noise ratio in
datasets. In particular, the test accuracy is a quadratic function of the noise
ratio in the case of symmetric noise, which explains the experimental findings
previously published. Based on our analysis, we apply cross-validation to
randomly split noisy datasets, which identifies most samples that have correct
labels. Then we adopt the Co-teaching strategy which takes full advantage of
the identified samples to train DNNs robustly against noisy labels. Compared
with extensive state-of-the-art methods, our strategy consistently improves the
generalization performance of DNNs under both synthetic and real-world training
noise.
