Collaborative learning with corrupted labels

Abstract Deep neural networks (DNNs) have been very successful for supervised learning. However, their high generalization performance often comes with the high cost of annotating data manually. Collecting low-quality labeled dataset is relatively cheap, e.g., using web search engines, while DNNs tend to overfit to corrupted labels easily. In this paper, we propose a collaborative learning (co-learning) approach to improve the robustness and generalization performance of DNNs on datasets with corrupted labels. This is achieved by designing a deep network with two separate branches, coupled with a relabelling mechanism. Co-learning could safely recover the true labels of most mislabeled samples, not only preventing the model from overfitting the noise, but also exploiting useful information from all the samples. Although being very simple, the proposed algorithm is able to achieve high generalization performance even a large portion of the labels are corrupted. Experiments show that co-learning consistently outperforms existing state-of-the-art methods on three widely used benchmark datasets.