论文标题
Standardgan：通过数据标准化对非常高分辨率卫星图像的语义分割的多源域适应
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
论文作者
论文摘要
最近对语义分割的域适应性进行了积极研究，以提高深度学习模型的概括能力。绝大多数域的适应方法应对单源案例，其中在单一源域上训练的模型适用于目标域。但是，这些方法的实际现实世界应用有限，因为通常一个具有不同数据分布的源域。在这项工作中，我们处理多源域的适应问题。我们的方法，即Standardgan，将每个源和目标域标准化，以使所有数据具有相似的数据分布。然后，我们使用标准化的源域来训练分类器并分段标准化的目标域。我们对两个遥感数据集进行了广泛的实验，其中第一个由一个国家的多个城市组成，而另一个国家则包含来自不同国家的多个城市。我们的实验结果表明，标准gan生成的标准化数据允许分类器产生更好的分割。
Domain adaptation for semantic segmentation has recently been actively studied to increase the generalization capabilities of deep learning models. The vast majority of the domain adaptation methods tackle single-source case, where the model trained on a single source domain is adapted to a target domain. However, these methods have limited practical real world applications, since usually one has multiple source domains with different data distributions. In this work, we deal with the multi-source domain adaptation problem. Our method, namely StandardGAN, standardizes each source and target domains so that all the data have similar data distributions. We then use the standardized source domains to train a classifier and segment the standardized target domain. We conduct extensive experiments on two remote sensing data sets, in which the first one consists of multiple cities from a single country, and the other one contains multiple cities from different countries. Our experimental results show that the standardized data generated by StandardGAN allow the classifiers to generate significantly better segmentation.
