  It has been known for a while that the problem of multi-source domain
adaptation can be regarded as a single source domain adaptation task where the
source domain corresponds to a mixture of the original source domains.
Nonetheless, how to adjust the mixture distribution weights remains an open
question. Moreover, most existing work on this topic focuses only on minimizing
the 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 current
state of the art by using a mildly optimistic objective function and
consistency regularization on the target samples.
