论文标题
同意还是在噪声互动的相互学习中分歧？
Agreement or Disagreement in Noise-tolerant Mutual Learning?
论文作者
论文摘要
深度学习在许多领域取得了许多显着的成就，但数据集中有嘈杂的标签。嘈杂的标签方法的最新学习共同教学和共同教学+通过双网络之间的相互信息面对嘈杂的标签。但是，双网络始终倾向于收敛，这会削弱双网机制以抵抗嘈杂标签。在本文中，我们以端到端的方式提出了一个名为MLC的耐噪声框架。它通过不同的正则化来调整双网络，以确保机制的有效性。此外，我们根据双网络之间的协议纠正标签分布。提出的方法可以利用嘈杂的数据来提高网络的准确性，概括和鲁棒性。我们在模拟嘈杂的数据集MNIST，CIFAR-10和现实世界嘈杂的数据集服装上测试了提出的方法1M。实验结果表明，我们的方法的表现优于先前的最新方法。此外，我们的方法是无网络的，因此它适用于许多任务。我们的代码可以在https://github.com/jiarunliu/mlc上找到。
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.
