Multi-Source-Free Unsupervised Domain Adaptation (MSFDA) aims to transfer
knowledge from multiple well-labeled source domains to an unlabeled target
domain, using source models instead of source data. Existing MSFDA methods
limited that each source domain provides only a single model, with a uniform
structure. This paper introduces a new MSFDA setting: Model-Agnostic
Multi-Source-Free Unsupervised Domain Adaptation (MMDA), allowing diverse
source models with varying architectures, without quantitative restrictions.
While MMDA holds promising potential, incorporating numerous source models
poses a high risk of including undesired models, which highlights the source
model selection problem. To address it, we first provide a theoretical analysis
of this problem. We reveal two fundamental selection principles:
transferability principle and diversity principle, and introduce a selection
algorithm to integrate them. Then, considering the measure of transferability
is challenging, we propose a novel Source-Free Unsupervised Transferability
Estimation (SUTE). This novel formulation enables the assessment and comparison
of transferability across multiple source models with different architectures
in the context of domain shift, without requiring access to any target labels
or source data. Based on the above, we introduce a new framework to address
MMDA. Specifically, we first conduct source model selection based on the
proposed selection principles. Subsequently, we design two modules to aggregate
knowledge from included models and recycle useful knowledge from excluded
models. These modules enable us to leverage source knowledge efficiently and
effectively, thereby supporting us in learning a discriminative target model
via adaptation. We validate the effectiveness of our method through numerous
experimental results, and demonstrate that our approach achieves
state-of-the-art performance.

多源无监督领域适应的模型选择问题和跨域转移能力估计的新方法，通过选择准则和新框架有效地学习鉴别性目标模型。

关于无需模型特定信息和多个源域的自适应领域迁移的研究

On the Model-Agnostic Multi-Source-Free Unsupervised Domain Adaptation

Source-free domain adaptation (SFDA) alleviates the domain discrepancy among
data obtained from domains without accessing the data for the awareness of data
privacy. However, existing conventional SFDA methods face inherent limitations
in medical contexts, where medical data are typically collected from multiple
institutions using various equipment. To address this problem, we propose a
simple yet effective method, named Uncertainty-aware Adaptive Distillation
(UAD) for the multi-source-free unsupervised domain adaptation (MSFDA) setting.
UAD aims to perform well-calibrated knowledge distillation from (i) model level
to deliver coordinated and reliable base model initialisation and (ii) instance
level via model adaptation guided by high-quality pseudo-labels, thereby
obtaining a high-performance target domain model. To verify its general
applicability, we evaluate UAD on two image-based diagnosis benchmarks among
two multi-centre datasets, where our method shows a significant performance
gain compared with existing works. The code will be available soon.

提出一种名为 UAD 的简单而有效方法，用于医学领域中的多源免监督域适应，通过模型层和实例层的知识蒸馏，实现高性能的目标领域模型，验证了其在两个图像诊断基准数据集上的普适性和显著性能提升。