Cooperative Learning for Noisy Supervision

Learning with noisy labels has gained the enormous interest in the robust deep learning area. Recent studies have empir-ically disclosed that utilizing dual networks can enhance the performance of single network but without theoretic proof. In this paper, we propose Cooperative Learning (CooL) frame-work for noisy supervision that analytically explains the ef-fects 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 indi-cate that CooL outperforms several state-of-the-art methods.