论文标题
pred＆指南：指导半监督域适应的标记目标类预测
Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation
论文作者
论文摘要
半监督域的适应性旨在通过利用富含标签的源域，几乎没有标记的目标域示例来对属于目标域的数据进行分类。在这里，我们提出了一个新颖的框架，Pred＆Guide，该框架利用了少数标记的目标示例的预测和实际类标签之间的不一致，以有效地指导域的适应性，以半手不足的设置。 pred＆指南由三个阶段组成，如下（1）首先，为了平等地对待所有目标样本，我们执行无监督的域适应性，并进行自我训练。 （2）第二是标签预测阶段，其中当前模型用于预测少数标记的目标示例的标签，（3）最后，标签预测的正确性用于有效地称量源示例，以更好地指导域适应过程。广泛的实验表明，提议的Pred＆Guide框架可为两个大规模基准数据集（即办公室家庭和域内）获得最新的结果。
Semi-supervised domain adaptation aims to classify data belonging to a target domain by utilizing a related label-rich source domain and very few labeled examples of the target domain. Here, we propose a novel framework, Pred&Guide, which leverages the inconsistency between the predicted and the actual class labels of the few labeled target examples to effectively guide the domain adaptation in a semi-supervised setting. Pred&Guide consists of three stages, as follows (1) First, in order to treat all the target samples equally, we perform unsupervised domain adaptation coupled with self-training; (2) Second is the label prediction stage, where the current model is used to predict the labels of the few labeled target examples, and (3) Finally, the correctness of the label predictions are used to effectively weigh source examples class-wise to better guide the domain adaptation process. Extensive experiments show that the proposed Pred&Guide framework achieves state-of-the-art results for two large-scale benchmark datasets, namely Office-Home and DomainNet.
