  Simulation-to-real domain adaptation for semantic segmentation has been
actively studied for various applications such as autonomous driving. Existing
methods mainly focus on a single-source setting, which cannot easily handle a
more practical scenario of multiple sources with different distributions. In
this paper, we propose to investigate multi-source domain adaptation for
semantic segmentation. Specifically, we design a novel framework, termed
Multi-source Adversarial Domain Aggregation Network (MADAN), which can be
trained in an end-to-end manner. First, we generate an adapted domain for each
source with dynamic semantic consistency while aligning at the pixel-level
cycle-consistently towards the target. Second, we propose sub-domain
aggregation discriminator and cross-domain cycle discriminator to make
different adapted domains more closely aggregated. Finally, feature-level
alignment is performed between the aggregated domain and target domain while
training the segmentation network. Extensive experiments from synthetic GTA and
SYNTHIA to real Cityscapes and BDDS datasets demonstrate that the proposed
MADAN model outperforms state-of-the-art approaches. Our source code is
released at: https://github.com/Luodian/MADAN.
