  Deep Learning with noisy labels is a practically challenging problem in
weakly supervised learning. The state-of-the-art approaches "Decoupling" and
"Co-teaching+" claim that the "disagreement" strategy is crucial for
alleviating the problem of learning with noisy labels. In this paper, we start
from a different perspective and propose a robust learning paradigm called
JoCoR, which aims to reduce the diversity of two networks during training.
Specifically, we first use two networks to make predictions on the same
mini-batch data and calculate a joint loss with Co-Regularization for each
training example. Then we select small-loss examples to update the parameters
of both two networks simultaneously. Trained by the joint loss, these two
networks would be more and more similar due to the effect of Co-Regularization.
Extensive experimental results on corrupted data from benchmark datasets
including MNIST, CIFAR-10, CIFAR-100 and Clothing1M demonstrate that JoCoR is
superior to many state-of-the-art approaches for learning with noisy labels.
