  This paper studies weakly supervised domain adaptation(WSDA) problem, where
we only have access to the source domain with noisy labels, from which we need
to transfer useful information to the unlabeled target domain. Although there
have been a few studies on this problem, most of them only exploit
unidirectional relationships from the source domain to the target domain. In
this paper, we propose a universal paradigm called GearNet to exploit bilateral
relationships between the two domains. Specifically, we take the two domains as
different inputs to train two models alternately, and asymmetrical
Kullback-Leibler loss is used for selectively matching the predictions of the
two models in the same domain. This interactive learning schema enables
implicit label noise canceling and exploits correlations between the source and
target domains. Therefore, our GearNet has the great potential to boost the
performance of a wide range of existing WSDL methods. Comprehensive
experimental results show that the performance of existing methods can be
significantly improved by equipping with our GearNet.
