Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the setting of a single source domain, where all the labeled source data come from the same distribution. However, in practice the labeled data can come from multiple source domains with different distributions. In such scenarios, the single source domain adaptation methods can fail due to the existence of domain shifts across different source domains and multi-source domain adaptation methods need to be designed. In this paper, we propose a novel multi-source domain adaptation method, Mutual Learning Network for Multiple Source Domain Adaptation (ML-MSDA). Under the framework of mutual learning, the proposed method pairs the target domain with each single source domain to train a conditional adversarial domain adaptation network as a branch network, while taking the pair of the combined multi-source domain and target domain to train a conditional adversarial adaptive network as the guidance network. The multiple branch networks are aligned with the guidance network to achieve mutual learning by enforcing JS-divergence regularization over their prediction probability distributions on the corresponding target data. We conduct extensive experiments on multiple multi-source domain adaptation benchmark datasets. The results show the proposed ML-MSDA method outperforms the comparison methods and achieves the state-of-the-art performance.

本文介绍一种新的多源域自适应方法—— Mutual Learning Network for Multiple Source Domain Adaptation (ML-MSDA)。通过互补学习的框架，该方法将目标域与每个单一源域配对，使用条件对抗性域自适应网络作为分支网络进行训练，并将组合后的多源域和目标域一起作为引导网络进行训练。多个分支网络与引导网络对齐，通过在相应目标数据上通过 JS 分歧正则化约束其预测概率分布来实现互补学习，该方法在多个多源域自适应基准数据集上进行了广泛的实验。结果表明，ML-MSDA方法优于比较方法，并实现了最新的性能表现。

多源领域自适应互相学习网络