  Unsupervised domain adaptation aims to transfer and adapt knowledge learned
from a labeled source domain to an unlabeled target domain. Key components of
unsupervised domain adaptation include: (a) maximizing performance on the
target, and (b) aligning the source and target domains. Traditionally, these
tasks have either been considered as separate, or assumed to be implicitly
addressed together with high-capacity feature extractors. When considered
separately, alignment is usually viewed as a problem of aligning data
distributions, either through geometric approaches such as subspace alignment
or through distributional alignment such as optimal transport. This paper
represents a hybrid approach, where we assume simplified data geometry in the
form of subspaces, and consider alignment as an auxiliary task to the primary
task of maximizing performance on the source. The alignment is made rather
simple by leveraging tractable data geometry in the form of subspaces. We
synergistically allow certain parameters derived from the closed-form auxiliary
solution, to be affected by gradients from the primary task. The proposed
approach represents a unique fusion of geometric and model-based alignment with
gradients from a data-driven primary task. Our approach termed SALT, is a
simple framework that achieves comparable or sometimes outperforms
state-of-the-art on multiple standard benchmarks.
