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.

提出了一种基于子空间识别理论的多源域自适应方法，它能在较少的约束条件下实现域不变和特定变量的脱钩，通过最小化域漂移对不变变量的影响来促进域适应。该方法在各种基准数据集上优于现有的多源域适应技术，凸显其在实际应用中的有效性。