Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Although current methods achieve target joint distribution identifiability by enforcing minimal changes across domains, they often necessitate stringent conditions, such as an adequate number of domains, monotonic transformation of latent variables, and invariant label distributions. These requirements are challenging to satisfy in real-world applications. To mitigate the need for these strict assumptions, we propose a subspace identification theory that guarantees the disentanglement of domain-invariant and domain-specific variables under less restrictive constraints regarding domain numbers and transformation properties, thereby facilitating domain adaptation by minimizing the impact of domain shifts on invariant variables. Based on this theory, we develop a Subspace Identification Guarantee (SIG) model that leverages variational inference. Furthermore, the SIG model incorporates class-aware conditional alignment to accommodate target shifts where label distributions change with the domains. Experimental results demonstrate that our SIG model outperforms existing MSDA techniques on various benchmark datasets, highlighting its effectiveness in real-world applications.

翻译：多源域适应（MSDA）方法旨在将知识从多个带标签的源域迁移至无标签的目标域。尽管现有方法通过强制域间最小变化实现了目标联合分布的可辨识性，但它们通常需要严苛的条件，例如足够的域数量、隐变量的单调变换以及标签分布不变性。这些要求在现实应用中难以满足。为缓解对严格假设的依赖，我们提出一种子空间辨识理论，该理论在域数量与变换属性方面限制较弱的条件下，保证域不变与域特异变量的解耦，从而通过最小化域偏移对不变变量的影响来促进域适应。基于该理论，我们开发了基于变分推断的子空间辨识保证（SIG）模型。此外，SIG模型引入类别感知的条件对齐，以适应标签分布随域变化的目标偏移。实验结果表明，我们的SIG模型在多个基准数据集上优于现有MSDA技术，凸显了其在现实应用中的有效性。