  We present an approach for unsupervised domain adaptation---with a strong
focus on practical considerations of within-domain class imbalance and
between-domain class distribution shift---from a class-conditioned domain
alignment perspective. Current methods for class-conditioned domain alignment
aim to explicitly minimize a loss function based on pseudo-label estimations of
the target domain. However, these methods suffer from pseudo-label bias in the
form of error accumulation. We propose a method that removes the need for
explicit optimization of model parameters from pseudo-labels directly. Instead,
we present a sampling-based implicit alignment approach, where the sample
selection procedure is implicitly guided by the pseudo-labels. Theoretical
analysis reveals the existence of a domain-discriminator shortcut in misaligned
classes, which is addressed by the proposed implicit alignment approach to
facilitate domain-adversarial learning. Empirical results and ablation studies
confirm the effectiveness of the proposed approach, especially in the presence
of within-domain class imbalance and between-domain class distribution shift.
