Adaptive Hardness-Driven Augmentation and Alignment Strategies for Multisource Domain Adaptations

Multisource domain adaptation (MDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Nevertheless, traditional methods primarily focus on achieving interdomain 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 intradomain alignment; and 3) the design of cluster-level constraints. In this article, we introduce a novel hardness-driven strategy for MDA tasks, named $\mathrm {A}^{3}\mathrm {MDA}$ , which collectively considers these three aspects through adaptive hardness quantification and utilization in both data augmentation and domain alignment. To achieve this, $\mathrm {A}^{3}\mathrm {MDA}$ progressively proposes three adaptive hardness measurements (AHMs), 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 interdomain alignment. As for the often-neglected intradomain alignment, we adaptively construct a pseudo-contrastive matrix (PCM) 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 $\mathrm {A}^{3}\mathrm {MDA}$ outperforms other methods.