  Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models
trained on multiple labeled source domains to an unlabeled target domain. In
this paper, we propose a novel multi-source domain adaptation framework based
on collaborative learning for semantic segmentation. Firstly, a simple image
translation method is introduced to align the pixel value distribution to
reduce the gap between source domains and target domain to some extent. Then,
to fully exploit the essential semantic information across source domains, we
propose a collaborative learning method for domain adaptation without seeing
any data from target domain. In addition, similar to the setting of
unsupervised domain adaptation, unlabeled target domain data is leveraged to
further improve the performance of domain adaptation. This is achieved by
additionally constraining the outputs of multiple adaptation models with pseudo
labels online generated by an ensembled model. Extensive experiments and
ablation studies are conducted on the widely-used domain adaptation benchmark
datasets in semantic segmentation. Our proposed method achieves 59.0\% mIoU on
the validation set of Cityscapes by training on the labeled Synscapes and GTA5
datasets and unlabeled training set of Cityscapes. It significantly outperforms
all previous state-of-the-arts single-source and multi-source unsupervised
domain adaptation methods.
