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Unsupervised domain adaptation aims to generalize the hypothesis trained in asource domain to an unlabeled target domain. One popular approach to thisproblem is to learn domain-invariant embeddings for both domains. In this work,we study, theoretically and empirically, the effect of the embedding complexityon generalization to the target domain. In particular, this complexity affectsan upper bound on the target risk; this is reflected in experiments, too. Next,we specify our theoretical framework to multilayer neural networks. As aresult, we develop a strategy that mitigates sensitivity to the embeddingcomplexity, and empirically achieves performance on par with or better than thebest layer-dependent complexity tradeoff.

The Role of Embedding Complexity in Domain-invariant Representations