  Learning with noisy labels has gained the enormous interest in the robust
deep learning area. Recent studies have empirically disclosed that utilizing
dual networks can enhance the performance of single network but without
theoretic proof. In this paper, we propose Cooperative Learning (CooL)
framework for noisy supervision that analytically explains the effects of
leveraging dual or multiple networks. Specifically, the simple but efficient
combination in CooL yields a more reliable risk minimization for unseen clean
data. A range of experiments have been conducted on several benchmarks with
both synthetic and real-world settings. Extensive results indicate that CooL
outperforms several state-of-the-art methods.
