  Domain adaptation (DA) tries to tackle the scenarios when the test data does
not fully follow the same distribution of the training data, and multi-source
domain adaptation (MSDA) is very attractive for real world applications. By
learning from large-scale unlabeled samples, self-supervised learning has now
become a new trend in deep learning. It is worth noting that both
self-supervised learning and multi-source domain adaptation share a similar
goal: they both aim to leverage unlabeled data to learn more expressive
representations. Unfortunately, traditional multi-task self-supervised learning
faces two challenges: (1) the pretext task may not strongly relate to the
downstream task, thus it could be difficult to learn useful knowledge being
shared from the pretext task to the target task; (2) when the same feature
extractor is shared between the pretext task and the downstream one and only
different prediction heads are used, it is ineffective to enable inter-task
information exchange and knowledge sharing. To address these issues, we propose
a novel \textbf{S}elf-\textbf{S}upervised \textbf{G}raph Neural Network (SSG),
where a graph neural network is used as the bridge to enable more effective
inter-task information exchange and knowledge sharing. More expressive
representation is learned by adopting a mask token strategy to mask some domain
information. Our extensive experiments have demonstrated that our proposed SSG
method has achieved state-of-the-art results over four multi-source domain
adaptation datasets, which have shown the effectiveness of our proposed SSG
method from different aspects.
