This work proposes a robust Partial Domain Adaptation (PDA) framework that mitigates the negative transfer problem by incorporating a robust target-supervision strategy. It leverages ensemble learning and includes diverse, complementary label feedback, alleviating the effect of incorrect feedback and promoting pseudo-label refinement. Rather than relying exclusively on first-order moments for distribution alignment, our approach offers explicit objectives to optimize intra-class compactness and inter-class separation with the inferred source prototypes and highly-confident target samples in a domain-invariant fashion. Notably, we ensure source data privacy by eliminating the need to access the source data during the adaptation phase through a priori inference of source prototypes. We conducted a series of comprehensive experiments, including an ablation analysis, covering a range of partial domain adaptation tasks. Comprehensive evaluations on benchmark datasets corroborate our framework's enhanced robustness and generalization, demonstrating its superiority over existing state-of-the-art PDA approaches.

翻译：本文提出了一种鲁棒的部分域适应（PDA）框架，通过融入稳健的目标监督策略来缓解负迁移问题。该框架利用集成学习并引入多样化、互补性的标签反馈，从而减轻错误反馈的影响并促进伪标签精炼。不同于单纯依赖一阶矩进行分布对齐，我们的方法通过推断源原型与高置信度目标样本，以域不变的方式提供显式目标来优化类内紧凑性与类间分离性。值得注意的是，我们通过先验推断源原型消除了适应阶段访问源数据的必要性，从而保障了源数据隐私。我们开展了一系列全面实验（包括消融分析），覆盖了多种部分域适应任务。在基准数据集上的综合评估证实了本框架在鲁棒性与泛化性方面的提升，证明了其相较于现有最先进PDA方法的优越性。