提出双专家协作学习框架，提升无源无监督域适应性能
A Tale of Two Experts: Cooperative Learning for Source-Free Unsupervised Domain Adaptation
- 双专家协同：冻结源模型与可训练文本提示的视觉语言模型并列挖掘目标数据知识
- 三阶段无监督训练：检索伪源与复杂目标样本，分别微调专家，再强制学习一致性
- 适用于隐私敏感场景，尤其适合无源数据时的模型迁移
无源无监督域适应（SFUDA）应对真实场景中无法获取源数据却需将源训练模型适配至目标域的挑战，源于对隐私与成本的顾虑。现有方法或仅利用源模型预测，或微调大型多模态模型，均忽视了互补信息与目标数据潜在结构。本文提出专家协作学习（EXCL），包含双专家框架与检索增强交互优化流程。双专家框架将冻结的源域模型（带Conv-Adapter增强）与预训练视觉语言模型（带可训练文本提示）置于同等地位，从无标签目标样本中挖掘共识知识。为在纯无监督条件下有效训练这些插件模块，引入三阶段检索增强交互（RAIN）：（1）协同检索伪源与复杂目标样本，（2）分别在各自样本集上微调每个专家，（3）通过共享学习结果强制学习一致性。在四个基准数据集上的大量实验表明，该方法达到当前最优性能。
原文摘要 · Abstract (English)
Source-Free Unsupervised Domain Adaptation (SFUDA) addresses the realistic challenge of adapting a source-trained model to a target domain without access to the source data, driven by concerns over privacy and cost. Existing SFUDA methods either exploit only the source model's predictions or fine-tune large multimodal models, yet both neglect complementary insights and the latent structure of target data. In this paper, we propose the Experts Cooperative Learning (EXCL). EXCL contains the Dual Experts framework and Retrieval-Augmentation-Interaction optimization pipeline. The Dual Experts framework places a frozen source-domain model (augmented with Conv-Adapter) and a pretrained vision-language model (with a trainable text prompt) on equal footing to mine consensus knowledge from unlabeled target samples. To effectively train these plug-in modules under purely unsupervised conditions, we introduce Retrieval-Augmented-Interaction(RAIN), a three-stage pipeline that (1) collaboratively retrieves pseudo-source and complex target samples, (2) separately fine-tunes each expert on its respective sample set, and (3) enforces learning object consistency via a shared learning result. Extensive experiments on four benchmark datasets demonstrate that our approach matches state-of-the-art performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。