Previous studies have shown that leveraging domain index can significantly boost domain adaptation performance \cite{arXiv:2007.01807, arXiv:2202.03628}. However, such domain indices are not always available. To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, and then propose an adversarial variational Bayesian framework that infers domain indices from multi-domain data, thereby providing additional insight on domain relations and improving domain adaptation performance. Our theoretical analysis shows that our adversarial variational Bayesian framework finds the optimal domain index at equilibrium. Empirical results on both synthetic and real data verify that our model can produce interpretable domain indices which enable us to achieve superior performance compared to state-of-the-art domain adaptation methods.

翻译：以往研究表明，利用领域索引可显著提升领域自适应性能\cite{arXiv:2007.01807, arXiv:2202.03628}，然而此类领域索引并非始终可获取。为应对这一挑战，我们首先从概率角度给出了领域索引的形式化定义，进而提出了一个对抗性变分贝叶斯框架，可从多领域数据中推断领域索引，从而提供关于领域关系的额外洞见并提升领域自适应性能。理论分析表明，我们的对抗性变分贝叶斯框架在平衡态时能求得最优领域索引。在合成数据与真实数据上的实验结果均证实，该模型可生成可解释的领域索引，使我们相较于现有最优领域自适应方法获得更优越的性能。