  In unsupervised domain adaptation (UDA), a classifier for the target domain
is trained with massive true-label data from the source domain and unlabeled
data from the target domain. However, collecting fully-true-label data in the
source domain is high-cost and sometimes impossible. Compared to the true
labels, a complementary label specifies a class that a pattern does not belong
to, hence collecting complementary labels would be less laborious than
collecting true labels. Thus, in this paper, we propose a novel setting that
the source domain is composed of complementary-label data, and a theoretical
bound for it is first proved. We consider two cases of this setting, one is
that the source domain only contains complementary-label data (completely
complementary unsupervised domain adaptation, CC-UDA), and the other is that
the source domain has plenty of complementary-label data and a small amount of
true-label data (partly complementary unsupervised domain adaptation, PC-UDA).
To this end, a complementary label adversarial network} (CLARINET) is proposed
to solve CC-UDA and PC-UDA problems. CLARINET maintains two deep networks
simultaneously, where one focuses on classifying complementary-label source
data and the other takes care of source-to-target distributional adaptation.
Experiments show that CLARINET significantly outperforms a series of competent
baselines on handwritten-digits-recognition and objects-recognition tasks.
