Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive
  Distillation
Yaxuan Song Jianan Fan Dongnan Liu Weidong Cai
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.