Graph-Relational Domain Adaptation

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 encodingconditioned 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. Generalization of machine learning methods hinges on the assumption that training and test data follows the same distribution. Such an assumption no longer holds when one trains a model in some domains (source domains), and tests it in other domains (target domains) where data follows different distributions. Domain adaptation (DA) aims at improving performance in this setting by aligning data from the source and target domains so that a model trained in source domains can generalize better in target domains (Ben-David et al., 2010; Ganin et al., 2016; Tzeng et al., 2017; Zhang et al., 2019). Left: Traditional DA treats other (Zhao et al., 2019; Wang et al., 2020). Such heterogeneity each domain equally and enforces uniform can often be captured by a graph, where the alignment for all domains, which is equivalent domains realize the nodes, and the adjacency between to enforcing a fully connected domain two domains can be captured by an edge (see Figure 1). Right: Our method generalizes traditional For example, to capture the similarity of weather in DA to align domains according to any the US, we can construct a graph where each state is specific domain graph, e.g., a domain graph treated as a node and the physical proximity between describing adjacency among these 15 states.