  Discrepancy between training and testing domains is a fundamental problem in
the generalization of machine learning techniques. Recently, several approaches
have been proposed to learn domain invariant feature representations through
adversarial deep learning. However, label shift, where the percentage of data
in each class is different between domains, has received less attention. Label
shift naturally arises in many contexts, especially in behavioral studies where
the behaviors are freely chosen. In this work, we propose a method called
Domain Adversarial nets for Target Shift (DATS) to address label shift while
learning a domain invariant representation. This is accomplished by using
distribution matching to estimate label proportions in a blind test set. We
extend this framework to handle multiple domains by developing a scheme to
upweight source domains most similar to the target domain. Empirical results
show that this framework performs well under large label shift in synthetic and
real experiments, demonstrating the practical importance.
