Single-Source Single-Target Domain Adaptation (1S1T) aims to bridge the gap between a labelled source domain and an unlabelled target domain. Despite 1S1T being a well-researched topic, they are typically not deployed to the real world. Methods like Multi-Source Domain Adaptation and Multi-Target Domain Adaptation have evolved to model real-world problems but still do not generalise well. The fact that most of these methods assume a common label-set between source and target is very restrictive. Recent Open-Set Domain Adaptation methods handle unknown target labels but fail to generalise in multiple domains. To overcome these difficulties, first, we propose a novel generic domain adaptation (DA) setting named Open-Set Multi-Source Multi-Target Domain Adaptation (OS-nSmT), with n and m being number of source and target domains respectively. Next, we propose a graph attention based framework named DEGAA which can capture information from multiple source and target domains without knowing the exact label-set of the target. We argue that our method, though offered for multiple sources and multiple targets, can also be agnostic to various other DA settings. To check the robustness and versatility of DEGAA, we put forward ample experiments and ablation studies.

翻译：单源单目标域自适应（1S1T）旨在弥合有标签源域与无标签目标域之间的差距。尽管1S1T已得到充分研究，但其通常无法部署到实际应用中。多源域自适应与多目标域自适应等方法虽已发展为模拟现实世界问题，但泛化能力仍显不足。多数此类方法假设源域与目标域共享相同标签集，这一限制极为严格。近期提出的开放集域自适应方法能够处理未知目标标签，却无法在多域场景下有效泛化。为克服上述困难，我们首先提出一种新颖的通用域自适应设置——开放集多源多目标域自适应（OS-nSmT，其中n和m分别表示源域与目标域数量）。继而，我们提出基于图注意力机制的框架DEGAA，该框架能够在无需知晓目标确切标签集的情况下，从多源域与多目标域中捕获信息。我们论证，尽管该方法专为多源多目标场景设计，但可独立于其他多种域自适应设置。为检验DEGAA的鲁棒性与通用性，我们开展了充分的实验与消融研究。