  Many variants of unsupervised domain adaptation (UDA) problems have been
proposed and solved individually. Its side effect is that a method that works
for one variant is often ineffective for or not even applicable to another,
which has prevented practical applications. In this paper, we give a general
representation of UDA problems, named Generalized Domain Adaptation (GDA). GDA
covers the major variants as special cases, which allows us to organize them in
a comprehensive framework. Moreover, this generalization leads to a new
challenging setting where existing methods fail, such as when domain labels are
unknown, and class labels are only partially given to each domain. We propose a
novel approach to the new setting. The key to our approach is self-supervised
class-destructive learning, which enables the learning of class-invariant
representations and domain-adversarial classifiers without using any domain
labels. Extensive experiments using three benchmark datasets demonstrate that
our method outperforms the state-of-the-art UDA methods in the new setting and
that it is competitive in existing UDA variations as well.
