  Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge
from a labeled source domain to an unlabeled target domain. Traditionally,
subspace-based methods form an important class of solutions to this problem.
Despite their mathematical elegance and tractability, these methods are often
found to be ineffective at producing domain-invariant features with complex,
real-world datasets. Motivated by the recent advances in representation
learning with deep networks, this paper revisits the use of subspace alignment
for UDA and proposes a novel adaptation algorithm that consistently leads to
improved generalization. In contrast to existing adversarial training-based DA
methods, our approach isolates feature learning and distribution alignment
steps, and utilizes a primary-auxiliary optimization strategy to effectively
balance the objectives of domain invariance and model fidelity. While providing
a significant reduction in target data and computational requirements, our
subspace-based DA performs competitively and sometimes even outperforms
state-of-the-art approaches on several standard UDA benchmarks. Furthermore,
subspace alignment leads to intrinsically well-regularized models that
demonstrate strong generalization even in the challenging partial DA setting.
Finally, the design of our UDA framework inherently supports progressive
adaptation to new target domains at test-time, without requiring retraining of
the model from scratch. In summary, powered by powerful feature learners and an
effective optimization strategy, we establish subspace-based DA as a highly
effective approach for visual recognition.
