论文标题
反对对抗性学习：自然区分开放式域中已知和未知
Against Adversarial Learning: Naturally Distinguish Known and Unknown in Open Set Domain Adaptation
论文作者
论文摘要
打开的设置域适应是指目标域包含源域中不存在的类别的情况。与典型的封闭设置域的适应相比，这是现实中更常见的情况，在该封闭域适应中，源域和目标域包含相同的类别。开放式设置域适应的主要困难是，当机器学习模型仅具有有关他们所知道的概念时，我们需要区分哪个目标数据属于未知类。在本文中，我们提出了一种“反对学习”方法，可以自然区分未知目标数据和已知数据，而无需设置任何其他超级参数，并且可以同时将预测到已知类别的目标数据分类。实验结果表明，与几种最新方法相比，所提出的方法可以显着改善性能。
Open set domain adaptation refers to the scenario that the target domain contains categories that do not exist in the source domain. It is a more common situation in the reality compared with the typical closed set domain adaptation where the source domain and the target domain contain the same categories. The main difficulty of open set domain adaptation is that we need to distinguish which target data belongs to the unknown classes when machine learning models only have concepts about what they know. In this paper, we propose an "against adversarial learning" method that can distinguish unknown target data and known data naturally without setting any additional hyper parameters and the target data predicted to the known classes can be classified at the same time. Experimental results show that the proposed method can make significant improvement in performance compared with several state-of-the-art methods.
