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It has been known for a while that the problem of multi-source domainadaptation can be regarded as a single source domain adaptation task where thesource domain corresponds to a mixture of the original source domains.Nonetheless, how to adjust the mixture distribution weights remains an openquestion. Moreover, most existing work on this topic focuses only on minimizingthe error on the source domains and achieving domain-invariant representations,which is insufficient to ensure low error on the target domain. In this work,we present a novel framework that addresses both problems and beats the currentstate of the art by using a mildly optimistic objective function andconsistency regularization on the target samples.

Tackling unsupervised multi-source domain adaptation with optimism and consistency