The performance of a machine-learning model is influenced by two main factors: the structure of the model, and the quality of the dataset it processes. As high-quality labeled data in substantial size is often difficult to obtain, there are ongoing efforts to develop machine learning algorithms that are robust with noisy datasets. Among these algorithms, multi-network learning utilizes learning from a noisy dataset by the selection and filtering of samples through multiple learning networks. We propose an improved co-teaching algorithm termed Co-TES that leverages different models with various architectures. Co-TES extracts different features from each iteration of data selection and makes the model more robust with the same quality dataset. Numerical results show that the proposed method can lead to faster performance gains in the early to mid-range.

中文翻译：

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Co-TES：通过共同教学交换生方法学习噪声标签

机器学习模型的性能受两个主要因素影响：模型的结构及其处理的数据集的质量。由于通常难以获得大量的高质量标记数据，因此人们一直在努力开发对噪声数据集具有鲁棒性的机器学习算法。在这些算法中，多网络学习通过多个学习网络选择和过滤样本，利用噪声数据集进行学习。我们提出了一种改进的协同教学算法，称为 Co-TES，它利用具有不同架构的不同模型。 Co-TES 从数据选择的每次迭代中提取不同的特征，并使模型在相同质量的数据集下更加稳健。数值结果表明，所提出的方法可以在早期到中期带来更快的性能提升。