Multi-source Domain Adaptation (MDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Nevertheless, traditional methods primarily focus on achieving inter-domain alignment through sample-level constraints, such as Maximum Mean Discrepancy (MMD), neglecting three pivotal aspects: 1) the potential of data augmentation, 2) the significance of intra-domain alignment, and 3) the design of cluster-level constraints. In this paper, we introduce a novel hardness-driven strategy for MDA tasks, named "A3MDA" , which collectively considers these three aspects through Adaptive hardness quantification and utilization in both data Augmentation and domain Alignment.To achieve this, "A3MDA" progressively proposes three Adaptive Hardness Measurements (AHM), i.e., Basic, Smooth, and Comparative AHMs, each incorporating distinct mechanisms for diverse scenarios. Specifically, Basic AHM aims to gauge the instantaneous hardness for each source/target sample. Then, hardness values measured by Smooth AHM will adaptively adjust the intensity level of strong data augmentation to maintain compatibility with the model's generalization capacity.In contrast, Comparative AHM is designed to facilitate cluster-level constraints. By leveraging hardness values as sample-specific weights, the traditional MMD is enhanced into a weighted-clustered variant, strengthening the robustness and precision of inter-domain alignment. As for the often-neglected intra-domain alignment, we adaptively construct a pseudo-contrastive matrix by selecting harder samples based on the hardness rankings, enhancing the quality of pseudo-labels, and shaping a well-clustered target feature space. Experiments on multiple MDA benchmarks show that " A3MDA " outperforms other methods.

翻译：多源域适应（MDA）旨在将多个已标注源域的知识迁移至未标注目标域。然而，传统方法主要通过样本级约束（如最大均值差异MMD）实现域间对齐，忽略了三个关键方面：1）数据增强的潜力；2）域内对齐的重要性；3）聚类级约束的设计。本文提出一种面向MDA任务的创新难度驱动策略，命名为“A3MDA”，该策略通过自适应难度量化及其在数据增强与域对齐中的协同运用，系统整合了上述三方面。为实现这一目标，“A3MDA”逐步构建了三种自适应难度度量（AHM），即基础型、平滑型与比较型AHM，每种度量针对不同场景融入差异化机制。具体而言，基础型AHM用于评估各源域/目标域样本的瞬时难度；平滑型AHM测得的难度值将自适应调节强数据增强的强度层级，以保持与模型泛化能力的兼容性；而比较型AHM则专为促进聚类级约束设计。通过将难度值作为样本特异性权重，传统MMD被增强为加权聚类变体，从而提升域间对齐的鲁棒性与精确度。针对常被忽视的域内对齐问题，我们依据难度排序筛选高难度样本，自适应构建伪对比矩阵，以此提升伪标签质量并塑造聚类结构清晰的目标特征空间。在多个MDA基准测试上的实验表明，“A3MDA”性能优于现有方法。