  Learning with noisy labels is one of the hottest problems in
weakly-supervised learning. Based on memorization effects of deep neural
networks, training on small-loss instances becomes very promising for handling
noisy labels. This fosters the state-of-the-art approach "Co-teaching" that
cross-trains two deep neural networks using the small-loss trick. However, with
the increase of epochs, two networks converge to a consensus and Co-teaching
reduces to the self-training MentorNet. To tackle this issue, we propose a
robust learning paradigm called Co-teaching+, which bridges the "Update by
Disagreement" strategy with the original Co-teaching. First, two networks feed
forward and predict all data, but keep prediction disagreement data only. Then,
among such disagreement data, each network selects its small-loss data, but
back propagates the small-loss data from its peer network and updates its own
parameters. Empirical results on benchmark datasets demonstrate that
Co-teaching+ is much superior to many state-of-the-art methods in the
robustness of trained models.

Machine Learning;Machine Learning