無監督域自适應尋求緩解源域和目标域之間的分布差異，給定源域的已标記樣本和目标域的未标記樣本。生成對抗網絡(GANs)通過生成特定于訓練領域的圖像，證明了在領域自适應方面的顯著改進。然而，大多數現有的基于GAN的無監督域自适應技術在域比對時沒有考慮語義資訊，是以當源域資料和目标域資料語義不同時，這些方法會降低性能。本文提出了一種端到端的新型語義一緻生成對抗網絡(SCGAN)。該網絡通過在特征層捕獲語義資訊，從源域和目标域生成無監督域自适應圖像，實作源域對目标域的比對。通過對數字和對象分類任務的實驗，我們證明了我們提出的方法的魯棒性，該方法在無監督的領域自适應設定中超過了目前最先進的性能。
原文題目：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.
基于生成對抗網絡的無監督域自适應中語義一緻性的保持.pdf