Most existing methods of domain adaptation are proposed for single-source settings, where the source data come from a single domain. However, in practice, the available data generally come from multiple domains with different distributions. In this paper, we propose asymmetric alignment joint consistent regularization (AACR) for multi-source domain adaptation. As for asymmetric alignment, we propose to learn asymmetric projections, explicitly treating each domain differently to avoid the loss of shared information. Data from different domains are encoded into a common subspace by these asymmetric projections, where the encoded features are further aligned to promote knowledge transfer. Further, we propose consistent regularization to better learn shared information and filter out domain-specific information. We formulate the two parts into a unified framework, and derive its global optimal solution. Comprehensive experiments are conducted to evaluate AACR, and the results verify the effectiveness and robustness of our method.

针对单源设置提出了大多数现有的域自适应方法，其中源数据来自单个域。但是，实际上，可用数据通常来自具有不同分布的多个域。在本文中，我们提出了用于多源域自适应的非对称对齐联合一致性正则化（AACR）。对于不对称对齐，我们建议学习不对称投影，显式地对每个域进行不同处理，以避免丢失共享信息。这些不对称投影将来自不同域的数据编码到一个公共子空间中，在这些子投影中，已编码的特征进一步对齐以促进知识转移。此外，我们建议进行一致的正则化，以更好地学习共享信息并过滤出特定于域的信息。我们将这两部分公式化为一个统一的框架，并导出其全局最优解。进行了全面的实验以评估AACR，结果验证了我们方法的有效性和鲁棒性。