无监督域自适应寻求缓解源域和目标域之间的分布差异，给定源域的已标记样本和目标域的未标记样本。生成对抗网络(GANs)通过生成特定于训练领域的图像，证明了在领域自适应方面的显著改进。然而，大多数现有的基于GAN的无监督域自适应技术在域匹配时没有考虑语义信息，因此当源域数据和目标域数据语义不同时，这些方法会降低性能。本文提出了一种端到端的新型语义一致生成对抗网络(SCGAN)。该网络通过在特征层捕获语义信息，从源域和目标域生成无监督域自适应图像，实现源域对目标域的匹配。通过对数字和对象分类任务的实验，我们证明了我们提出的方法的鲁棒性，该方法在无监督的领域自适应设置中超过了目前最先进的性能。
原文题目：Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks
原文：Unsupervised domain adaptation seeks to mitigate the distribution discrepancy between source and target domains, given labeled samples of the source domain and unlabeled samples of the target domain. Generative adversarial networks (GANs) have demonstrated significant improvement in domain adaptation by producing images which are domain specific for training. However, most of the existing GAN based techniques for unsupervised domain adaptation do not consider semantic information during domain matching, hence these methods degrade the performance when the source and target domain data are semantically different. In this paper, we propose an end-to-end novel semantic consistent generative adversarial network (SCGAN). This network can achieve source to target domain matching by capturing semantic information at the feature level and producing images for unsupervised domain adaptation from both the source and the target domains. We demonstrate the robustness of our proposed method which exceeds the state-of-the-art performance in unsupervised domain adaptation settings by performing experiments on digit and object classification tasks.
基于生成对抗网络的无监督域自适应中语义一致性的保持.pdf