We use information-theoretic tools to derive a novel analysis of Multi-source
Domain Adaptation (MDA) from the representation learning perspective.
Concretely, we study joint distribution alignment for supervised MDA with few
target labels and unsupervised MDA with pseudo labels, where the latter is
relatively hard and less commonly studied. We further provide
algorithm-dependent generalization bounds for these two settings, where the
generalization is characterized by the mutual information between the
parameters and the data. Then we propose a novel deep MDA algorithm, implicitly
addressing the target shift through joint alignment. Finally, the mutual
information bounds are extended to this algorithm providing a non-vacuous
gradient-norm estimation. The proposed algorithm has comparable performance to
the state-of-the-art on target-shifted MDA benchmark with improved memory
efficiency.

使用信息论工具从表示学习的角度推导了多源域自适应（MDA）的新型分析。具体而言，研究了具有少量目标标签的监督 MDA 和具有伪标签的无监督 MDA 的联合分布对齐，提出了一种隐式处理目标偏移的新型深度 MDA 算法，最后扩展了相互信息边界以提供一种非平凡的梯度范数估计。所提出的算法在目标偏移 MDA 基准测试中具有与最先进技术相当的性能，并具有改进的内存效率。