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Jan, 2019
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A review of single-source unsupervised domain adaptation
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Wouter M. Kouw, Marco Loog
TL;DR
本篇综述旨在回答分类器如何从源域学习并推广到目标域，分类方法包括基于样本的方法、基于特征的方法和基于推理的方法，文章还探讨了进一步研究所需的一些问题和条件。
Abstract
domain adaptation
has become a prominent problem setting in
machine learning
and related fields. This review asks the questions: when and how a classifier can learn from a source domain and generalize to a target
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