论文标题
通过同时转移源知识和目标相关性，无监督的域适应
Unsupervised Domain Adaptation Through Transferring both the Source-Knowledge and Target-Relatedness Simultaneously
论文作者
论文摘要
无监督的域适应性（UDA）是机器学习和模式识别领域的新兴研究主题，该主题旨在通过从源域转移知识来帮助学习未标记的目标域。
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.
