Domain adaptation, which transfers the knowledge from label-rich source domain to unlabeled target domains, is a challenging task in machine learning. The prior domain adaptation methods focus on pairwise adaptation assumption with a single source and a single target domain, while little work concerns the scenario of one source domain and multiple target domains. Applying pairwise adaptation methods to this setting may be suboptimal, as they fail to consider the semantic association among multiple target domains. In this work we propose a deep semantic information propagation approach in the novel context of multiple unlabeled target domains and one labeled source domain. Our model aims to learn a unified subspace common for all domains with a heterogeneous graph attention network, where the transductive ability of the graph attention network can conduct semantic propagation of the related samples among multiple domains. In particular, the attention mechanism is applied to optimize the relationships of multiple domain samples for better semantic transfer. Then, the pseudo labels of the target domains predicted by the graph attention network are utilized to learn domain-invariant representations by aligning labeled source centroid and pseudo-labeled target centroid. We test our approach on four challenging public datasets, and it outperforms several popular domain adaptation methods.

中文翻译：

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用于无监督多目标域自适应的异构图注意网络

域适应，将知识从标签丰富的源域转移到未标记的目标域，是机器学习中的一项具有挑战性的任务。现有的域自适应方法侧重于具有单个源和单个目标域的成对自适应假设，而很少有工作涉及一个源域和多个目标域的场景。将成对自适应方法应用于此设置可能不是最理想的，因为它们没有考虑多个目标域之间的语义关联。在这项工作中，我们在多个未标记的目标域和一个标记的源域的新上下文中提出了一种深度语义信息传播方法。我们的模型旨在通过异构图注意力网络学习所有领域通用的统一子空间，其中图注意力网络的转导能力可以在多个域之间进行相关样本的语义传播。特别是，注意力机制被应用于优化多个领域样本的关系，以实现更好的语义转移。然后，利用图注意力网络预测的目标域的伪标签，通过对齐标记的源质心和伪标记的目标质心来学习域不变表示。我们在四个具有挑战性的公共数据集上测试了我们的方法，它优于几种流行的领域适应方法。由图注意力网络预测的目标域的伪标签通过对齐标记的源质心和伪标记的目标质心来学习域不变表示。我们在四个具有挑战性的公共数据集上测试了我们的方法，它优于几种流行的领域适应方法。由图注意力网络预测的目标域的伪标签通过对齐标记的源质心和伪标记的目标质心来学习域不变表示。我们在四个具有挑战性的公共数据集上测试了我们的方法，它优于几种流行的领域适应方法。