  Domain adaptation aims to learn a transferable model to bridge the domain
shift between one labeled source domain and another sparsely labeled or
unlabeled target domain. Since the labeled data may be collected from multiple
sources, multi-source domain adaptation (MDA) has attracted increasing
attention. Recent MDA methods do not consider the pixel-level alignment between
sources and target or the misalignment across different sources. In this paper,
we propose a novel MDA framework to address these challenges. Specifically, we
design an end-to-end Multi-source Adversarial Domain Aggregation Network
(MADAN). First, an adapted domain is generated for each source with dynamic
semantic consistency while aligning towards the target at the pixel-level
cycle-consistently. Second, sub-domain aggregation discriminator and
cross-domain cycle discriminator are proposed to make different adapted domains
more closely aggregated. Finally, feature-level alignment is performed between
the aggregated domain and the target domain while training the task network.
For the segmentation adaptation, we further enforce category-level alignment
and incorporate context-aware generation, which constitutes MADAN+. We conduct
extensive MDA experiments on digit recognition, object classification, and
simulation-to-real semantic segmentation. The results demonstrate that the
proposed MADAN and MANDA+ models outperform state-of-the-art approaches by a
large margin.
