  Noisy labels, resulting from mistakes in manual labeling or webly data
collecting for supervised learning, can cause neural networks to overfit the
misleading information and degrade the generalization performance.
Self-supervised learning works in the absence of labels and thus eliminates the
negative impact of noisy labels. Motivated by co-training with both supervised
learning view and self-supervised learning view, we propose a simple yet
effective method called Co-learning for learning with noisy labels. Co-learning
performs supervised learning and self-supervised learning in a cooperative way.
The constraints of intrinsic similarity with the self-supervised module and the
structural similarity with the noisily-supervised module are imposed on a
shared common feature encoder to regularize the network to maximize the
agreement between the two constraints. Co-learning is compared with peer
methods on corrupted data from benchmark datasets fairly, and extensive results
are provided which demonstrate that Co-learning is superior to many
state-of-the-art approaches.
