  Deep learning with noisy labels is challenging as deep neural networks have
the high capacity to memorize the noisy labels. In this paper, we propose a
learning algorithm called Co-matching, which balances the consistency and
divergence between two networks by augmentation anchoring. Specifically, we
have one network generate anchoring label from its prediction on a
weakly-augmented image. Meanwhile, we force its peer network, taking the
strongly-augmented version of the same image as input, to generate prediction
close to the anchoring label. We then update two networks simultaneously by
selecting small-loss instances to minimize both unsupervised matching loss
(i.e., measure the consistency of the two networks) and supervised
classification loss (i.e. measure the classification performance). Besides, the
unsupervised matching loss makes our method not heavily rely on noisy labels,
which prevents memorization of noisy labels. Experiments on three benchmark
datasets demonstrate that Co-matching achieves results comparable to the
state-of-the-art methods.
