发表机构
Istituto Italiano di Tecnologia; University of Genoa; Nanjing University; University of Verona（意大利理工学院; 热那亚大学; 南京大学; 维罗纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MUSE框架，通过解耦语义与风格适应，实现单次扩散微调生成多目标域数据，在UDA基准上提升精度-效率权衡。
AI 中文摘要
基于扩散的无监督域适应（UDA）通过为下游适应生成目标特定的合成数据来改善跨域迁移。现有方法主要针对单目标适应设计：当在一个标记源域上训练的模型必须适应多个未标记目标域时，它们通常需要对每个源-目标对进行单独的扩散微调，导致训练、存储和部署成本随目标数量增加而增长。在本文中，我们研究了基于扩散的UDA的多目标数据生成，其中单个源引导的扩散微调过程被重用以为多个目标域生成目标特定的合成数据。我们提出了MUSE（多目标UDA导向的合成与高效扩散微调），一种解耦的适应框架，将源监督的语义适应与目标特定的风格适应分开。MUSE使用由标记源数据更新的共享语义分支和专用于各个目标域的目标私有风格分支，从而实现目标特定的生成，同时避免为每个目标重复进行源引导的微调。在标准UDA基准上的实验表明，MUSE比重复的每目标扩散适应实现了更强的精度-效率权衡，降低了扩散微调成本，同时提高了平均目标域精度。项目页面可在该https URL获取。
英文摘要
Diffusion-based unsupervised domain adaptation (UDA) improves cross-domain transfer by generating target-specific synthetic data for downstream adaptation. Existing methods are largely designed for single-target adaptation: when a model trained on one labeled source domain must be adapted to multiple unlabeled target domains, they typically require separate diffusion fine-tuning for each source--target pair, causing training, storage, and deployment costs to grow with the number of targets. In this paper, we study multi-target data generation for diffusion-based UDA, where a single source-guided diffusion fine-tuning process is reused to generate target-specific synthetic data for multiple target domains. We propose MUSE (Multi-target UDA-oriented Synthesis with Efficient diffusion fine-tuning), a decoupled adaptation framework that separates source-supervised semantic adaptation from target-specific style adaptation. MUSE uses a shared semantic branch updated by labeled source data and target-private style branches specialized to individual target domains, enabling target-specific generation while avoiding repeated source-guided fine-tuning for each target. Experiments on standard UDA benchmarks show that MUSE achieves a stronger accuracy--efficiency trade-off than repeated per-target diffusion adaptation, reducing diffusion fine-tuning cost while improving average target-domain accuracy. The project page is available at https://xuanqi99.github.io/MUSE/.
CommentsAccepted at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
