  In unsupervised domain adaptation (UDA), classifiers for the target domain
are trained with massive true-label data from the source domain and unlabeled
data from the target domain. However, it may be difficult to collect
fully-true-label data in a source domain given a limited budget. To mitigate
this problem, we consider a novel problem setting where the classifier for the
target domain has to be trained with complementary-label data from the source
domain and unlabeled data from the target domain named budget-friendly UDA
(BFUDA). The key benefit is that it is much less costly to collect
complementary-label source data (required by BFUDA) than collecting the
true-label source data (required by ordinary UDA). To this end, the
complementary label adversarial network (CLARINET) is proposed to solve the
BFUDA problem. CLARINET maintains two deep networks simultaneously, where one
focuses on classifying complementary-label source data and the other takes care
of the source-to-target distributional adaptation. Experiments show that
CLARINET significantly outperforms a series of competent baselines.
