In this paper, we propose to tackle the problem of reducing discrepancies
between multiple domains referred to as multi-source domain adaptation and
consider it under the target shift assumption: in all domains we aim to solve a
classification problem with the same output classes, but with labels'
proportions differing across them. This problem, generally ignored in the vast
majority papers on domain adaptation papers, is nevertheless critical in
real-world applications, and we theoretically show its impact on the adaptation
success. To address this issue, we design a method based on optimal transport,
a theory that has been successfully used to tackle adaptation problems in
machine learning. Our method performs multi-source adaptation and target shift
correction simultaneously by learning the class probabilities of the unlabeled
target sample and the coupling allowing to align two (or more) probability
distributions. Experiments on both synthetic and real-world data related to
satellite image segmentation task show the superiority of the proposed method
over the state-of-the-art.

本文提出了一种基于最优传输的多源域自适应方法解决目标移位下的分类问题，实验结果表明相比现有技术在卫星图像分割任务上具有较好的性能。