Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. While recent MUDA methods have shown promising results, most focus on aligning the overall feature distributions across source domains, which can lead to negative effects due to redundant features within each domain. Moreover, there is a significant performance gap between MUDA and supervised methods. To address these challenges, we propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA). Firstly, we establish a multi-source dynamic modulation mechanism during the training process based on the degree of distribution differences between source and target domains. This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains. Additionally, we propose a Multi-source Active Boundary Sample Selection (MABS) strategy, which utilizes a guided dynamic boundary loss to design an efficient query function for selecting important samples. This strategy achieves improved generalization to the target domain with minimal sampling costs. We extensively evaluate our proposed method on commonly used domain adaptation datasets, comparing it against existing UDA and ADA methods. The experimental results unequivocally demonstrate the superiority of our approach.

翻译：多源无监督域适应（MUDA）旨在从相关的源域将知识迁移至未标记的目标域。尽管近期MUDA方法取得了显著成果，大多数方法侧重于对齐各源域的整体特征分布，这可能导致因各域内冗余特征而产生的负面影响。此外，MUDA与有监督方法之间仍存在显著的性能差距。为应对这些挑战，我们提出了一种名为“面向主动多域适配的动态域差异调整”（D3AAMDA）的新方法。首先，我们基于源域与目标域分布差异程度，在训练过程中建立了一种多源动态调节机制。该机制可控制每个源域与目标域特征的对齐程度，从而有效利用源域内的局部优势特征信息。此外，我们提出了一种多源主动边界样本选择（MABS）策略，该策略利用引导式动态边界损失来设计高效的查询函数，以选取重要样本。该策略能以最小采样成本实现对目标域的泛化性能提升。我们在常用域适应数据集上对所提方法进行了全面评估，并与现有无监督域适应（UDA）和主动域适应（ADA）方法进行了对比。实验结果明确证明了我们方法的优越性。