IEEE Trans Image Process. 2016 Dec;25(12):5552-5562. doi: 10.1109/TIP.2016.2609820. Epub 2016 Sep 15.
Unsupervised domain adaptation deals with scenarios in which labeled data are available in the source domain, but only unlabeled data can be observed in the target domain. Since the classifiers trained by source-domain data would not be expected to generalize well in the target domain, how to transfer the label information from source to target-domain data is a challenging task. A common technique for unsupervised domain adaptation is to match cross-domain data distributions, so that the domain and distribution differences can be suppressed. In this paper, we propose to utilize the label information inferred from the source domain, while the structural information of the unlabeled target-domain data will be jointly exploited for adaptation purposes. Our proposed model not only reduces the distribution mismatch between domains, improved recognition of target-domain data can be achieved simultaneously. In the experiments, we will show that our approach performs favorably against the state-of-the-art unsupervised domain adaptation methods on benchmark data sets. We will also provide convergence, sensitivity, and robustness analysis, which support the use of our model for cross-domain classification.
无监督领域自适应处理的是这样一种场景，在该场景中，源域中存在标记数据，但目标域中只能观察到未标记数据。由于源域数据训练的分类器预计在目标域中不能很好地泛化，因此如何将标签信息从源域传输到目标域数据是一项具有挑战性的任务。一种常见的无监督领域自适应技术是匹配跨域数据分布，从而可以抑制域和分布差异。在本文中，我们提出利用源域推断出的标签信息，同时联合利用未标记目标域数据的结构信息进行自适应。我们的模型不仅减少了域之间的分布不匹配，同时还能提高对目标域数据的识别能力。在实验中，我们将表明，我们的方法在基准数据集上优于最先进的无监督领域自适应方法。我们还将提供收敛性、敏感性和稳健性分析，这支持了我们的模型在跨域分类中的使用。
