  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.
