  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.
