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.

本文提出了一个鲁棒的部分领域自适应（PDA）框架，通过结合鲁棒的目标监督策略来缓解负迁移问题。它利用集成学习并包括多样的、互补的标签反馈，减轻了错误反馈的影响并促进了伪标签的改进。与仅依赖于分布对齐的一阶矩不同，我们的方法通过源样本原型和高置信度目标样本，在域不变的方式下提供了明确的目标来优化类内紧凑性和类间分离性。值得注意的是，我们通过先验地推断源样本原型来确保源数据的隐私，从而在自适应阶段不需要访问源数据。我们进行了一系列全面的实验证明分析，涵盖了一系列部分领域自适应任务。对基准数据集的全面评估证实了我们的框架的增强鲁棒性和泛化性，展示了其相对于现有最先进PDA方法的优越性。

一种使用源原型的健壮负学习方法应用于部分领域适应