Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; therefore it may be beneficial for nearby domains, but not necessarily for distant domains. In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e.g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure. We generalize the existing adversarial learning framework with a novel graph discriminator using encoding-conditioned graph embeddings. Theoretical analysis shows that at equilibrium, our method recovers classic domain adaptation when the graph is a clique, and achieves non-trivial alignment for other types of graphs. Empirical results show that our approach successfully generalizes uniform alignment, naturally incorporates domain information represented by graphs, and improves upon existing domain adaptation methods on both synthetic and real-world datasets. Code will soon be available at https://github.com/Wang-ML-Lab/GRDA.

翻译：现有域自适应方法往往平等对待每个域，并追求所有域间的完美对齐。这种统一对齐忽略了不同域之间的拓扑结构，因此可能对邻近域有益，但对相距较远的域未必有效。在本研究中，我们通过使用域图来编码域邻接关系（例如，美国各州构成的图，每个州为一个域，每条边表示邻接关系），从而放宽了这种统一对齐，使得域能够根据图结构灵活地对齐。我们通过一种新奇的图判别器，利用编码条件图嵌入，对现有的对抗学习框架进行了推广。理论分析表明，在平衡状态下，当图为完全图时，我们的方法能恢复经典域自适应，而对于其他类型的图，则能实现非平凡的对齐。实证结果表明，我们的方法成功推广了统一对齐，自然地融入了由图表示的域信息，并在合成数据集和真实世界数据集上均优于现有的域自适应方法。代码即将在 https://github.com/Wang-ML-Lab/GRDA 发布。