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 a novel 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 multi-scale image generation, which constitutes MADAN+. We conduct extensive MDA experiments on digit recognition, object classification, and simulation-to-real semantic segmentation tasks. The results demonstrate that the proposed MADAN and MADAN+ models outperform state-of-the-art approaches by a large margin.

域自适应旨在学习一种可转移的模型，以桥接一个标记的源域与另一个稀疏标记或未标记的目标域之间的域转换。由于可以从多个源收集标记的数据，因此多源域适配（MDA）引起了越来越多的关注。最新的MDA方法没有考虑源与目标之间的像素级对齐或不同源之间的未对齐。在本文中，我们提出了一个新颖的MDA框架来应对这些挑战。具体来说，我们设计了一个新颖的多源对抗域聚合网络（MADAN）。首先，为每个源生成一个具有动态语义一致性的自适应域，同时以像素级周期一致地朝目标对齐。第二，提出了子域聚集鉴别符和跨域循环鉴别符，以使不同的适应域更紧密地聚集。最后，在训练任务网络的同时，在聚合域和目标域之间执行功能级别的对齐。对于细分适应，我们进一步强制进行类别级对齐，并合并构成MADAN +的多尺度图像生成。我们在数字识别，对象分类和模拟到真实的语义分割任务上进行了广泛的MDA实验。结果表明，提出的MADAN和MADAN +模型在很大程度上优于最新方法。