Unsupervised Domain Adaptation Through Transferring both the Source-Knowledge and Target-Relatedness Simultaneously
arXiv:2003.08051v32 citations
AI Analysis
This addresses the problem of adapting models across domains without labeled target data, which is incremental as it builds on existing UDA methods.
The paper tackles unsupervised domain adaptation by transferring both source knowledge and target-relatedness simultaneously, aiming to improve learning in unlabeled target domains, but no concrete results or numbers are provided in the abstract.
Unsupervised domain adaptation (UDA) is an emerging research topic in the field of machine learning and pattern recognition, which aims to help the learning of unlabeled target domain by transferring knowledge from the source domain.