Agreement or Disagreement in Noise-tolerant Mutual Learning?
This work addresses noisy labels in datasets, a common issue in deep learning, by enhancing existing dual-network methods to prevent convergence and improve performance, though it appears incremental as it builds on Co-teaching and Co-teaching+.
The paper tackles the problem of noisy labels in deep learning by proposing a noise-tolerant framework (MLC) that uses divergent regularization on dual networks and label correction based on network agreement, achieving improved accuracy, generalization, and robustness, with experimental results showing it outperforms previous state-of-the-art methods on datasets like MNIST, CIFAR-10, and Clothing1M.
Deep learning has made many remarkable achievements in many fields but suffers from noisy labels in datasets. The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information between dual-network. However, the dual network always tends to convergent which would weaken the dual-network mechanism to resist the noisy labels. In this paper, we proposed a noise-tolerant framework named MLC in an end-to-end manner. It adjusts the dual-network with divergent regularization to ensure the effectiveness of the mechanism. In addition, we correct the label distribution according to the agreement between dual-networks. The proposed method can utilize the noisy data to improve the accuracy, generalization, and robustness of the network. We test the proposed method on the simulate noisy dataset MNIST, CIFAR-10, and the real-world noisy dataset Clothing1M. The experimental result shows that our method outperforms the previous state-of-the-art method. Besides, our method is network-free thus it is applicable to many tasks. Our code can be found at https://github.com/JiarunLiu/MLC.