The content discusses the challenges and solutions in Model-Agnostic Multi-Source-Free Unsupervised Domain Adaptation (MMDA). It introduces a novel Source-Free Unsupervised Transferability Estimation (SUTE) method to assess transferability across multiple source models. The Selection, Aggregation, and Adaptation (SAA) framework is proposed for efficient knowledge utilization. Experimental validation demonstrates state-of-the-art performance.
The paper addresses the limitations of existing methods in unsupervised domain adaptation by introducing MMDA. It emphasizes the significance of selecting appropriate source models based on transferability and diversity principles. The proposed SUTE method enables effective assessment of transferability without target labels. The SAA framework optimizes knowledge aggregation for improved adaptation performance.
To Another Language
from source content
arxiv.org
Djupare frågor