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：多源域自适应方法旨在将多个有标签源域的知识迁移至无标签目标域。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，但它们往往需要严格条件，例如足够的域数量、潜变量的单调变换以及不变的标签分布。这些要求在实际应用中难以满足。为缓解对这些严格假设的需求，我们提出一种子空间辨识理论，该理论在域数量和变换性质的约束较少的情况下，保证域不变变量与域特定变量的解耦，从而通过最小化域偏移对不变变量的影响促进域自适应。基于该理论，我们开发了利用变分推理的子空间辨识保证模型。此外，SIG模型通过引入类别感知条件对齐来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，突显了其在实际应用中的有效性。