Current unsupervised domain adaptation (UDA) methods based on GAN (Generative Adversarial Network) architectures assume that source samples arise from a single distribution. These methods have shown compelling results by finding the transformation between source and target domains to reduce the distribution divergence. However, the one-to-one assumption renders the existing GAN-based UDA methods ineffective in a more realistic scenario that source samples are typically collected from diverse sources. In this paper, we present a novel GAN-enabled framework, which we call Multi-Source Adaptation Network (MSAN), for multiple-source domain adaptation (MDA) to mitigate the domain shifts between multiple source domains and the target domain. The proposed framework consists of multiple GAN architectures to learn bidirectional transformations between the source domains and the target domain efficiently and simultaneously. Technically, we introduce a joint feature space to guide the multi-level consistency constraints across all the transformations, in order to preserve the domain-invariant pattern and endow the discriminative power for the unlabeled target samples simultaneously during the adaptation. Moreover, the proposed model can naturally be used to enlarge the target dataset by utilizing the synthetic target images (with ground-truth labels from different source domains) and the pseudo-labeled target images, thereby allowing constructing the target-specific classifier in an unsupervised manner. Experiments demonstrate that our models exceed state-of-the-art results for MDA tasks on several benchmark datasets.

当前基于GAN（通用对抗网络）体系结构的无监督域自适应（UDA）方法假定源样本来自单个分布。这些方法通过找到源域和目标域之间的转换以减少分布差异而显示出令人信服的结果。但是，一对一的假设使现有的基于GAN的UDA方法在更现实的情况下无效，该情况通常是从各种来源收集源样本。在本文中，我们提出了一种新颖的支持GAN的框架，我们将其称为多源自适应网络（MSAN），用于多源域自适应（MDA），以减轻多个源域与目标域之间的域转换。所提出的框架由多个GAN架构组成，以有效且同时地学习源域和目标域之间的双向转换。从技术上讲，我们引入了一个联合特征空间来指导所有转换之间的多级一致性约束，以便在适应过程中同时保留域不变模式并同时赋予未标记目标样本以判别能力。此外，通过利用合成目标图像（带有来自不同源域的地面标签）和伪标记的目标图像，建议的模型自然可以用于扩展目标数据集，从而允许在无监督的情况下构造目标特定的分类器方式。