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 true-label data in the source domain can be expensive and sometimes impractical. Compared to the true label (TL), a complementary label (CL) specifies a class that a pattern does not belong to, and hence, collecting CLs would be less laborious than collecting TLs. In this article, we propose a novel setting where the source domain is composed of complementary-label data, and a theoretical bound of this setting is provided. We consider two cases of this setting: one is that the source domain only contains complementary-label data [completely complementary UDA (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 UDA (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, with one focusing on classifying the complementary-label source data and the other taking care of the source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines on handwritten digit-recognition and object-recognition tasks.

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 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, 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.