Learning From a Complementary-Label Source Domain: Theory and Algorithms

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.