✅ Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation

science

:2411.07185v1 Announce Type: cross Abstract: Multi-source unsupervised domain adaptation aims to leverage labeled data from multiple source domains for training a machine learning model to generalize well on a target domain without labels. Source domain selection plays a crucial role in determining the model's performance. It relies on the similarities amongst source and target domains. Nonetheless, existing work for source domain selection often involves heavyweight computational procedures, especially when dealing with numerous source domains and the need to identify the best ones from them. In this paper, we introduce a framework for gradual fine tuning (GFT) of machine learning models on multiple source domains.

arXiv     Nov 12, 2024