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:单一源单一目标域适应 (S1T) 旨在缩小标签源域和未标签目标域之间的差距。 尽管 1S1T 是研究周全的话题, 但通常不会在现实世界中部署。 多源域适应和多目标域适应等方法已经演变成模拟真实世界问题, 但仍不能一概而论。 大多数这些方法在源和目标之间假定一个共同的标签设置是非常限制性的。 最近开放域适应方法处理未知的目标标签, 但却无法在多个领域推广。 首先, 我们提出一个新的通用域适应(DA), 设置名为 Open- Set多源多目标多目标适应(OS- nSmet), 分别是源和目标区域的数目。 其次, 我们提议一个以图表为基础的关注框架, 这个框架可以在不知道目标准确的标签设置的情况下从多个源和目标域获取信息。 我们提出的方法虽然针对多个来源和多个目标, 但无法在多个领域推广。 首先, 我们提议一个创新的通用域适应(DGA), 也可以对多种来源和DA 的多端点进行快速的实验。