  Theoretically, domain adaptation is a well-researched problem. Further, this
theory has been well-used in practice. In particular, we note the bound on
target error given by Ben-David et al. (2010) and the well-known
domain-aligning algorithm based on this work using Domain Adversarial Neural
Networks (DANN) presented by Ganin and Lempitsky (2015). Recently, multiple
variants of DANN have been proposed for the related problem of domain
generalization, but without much discussion of the original motivating bound.
In this paper, we investigate the validity of DANN in domain generalization
from this perspective. We investigate conditions under which application of
DANN makes sense and further consider DANN as a dynamic process during
training. Our investigation suggests that the application of DANN to domain
generalization may not be as straightforward as it seems. To address this, we
design an algorithmic extension to DANN in the domain generalization case. Our
experimentation validates both theory and algorithm.
