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。

多源领域自适应表示学习的算法相关性界限