  Unsupervised domain adaptation (UDA) tries to overcome the need for a large
labeled dataset by transferring knowledge from a source dataset, with lots of
labeled data, to a target dataset, that has no labeled data. Since there are no
labels in the target domain, early misalignment might propagate into the later
stages and lead to an error build-up. In order to overcome this problem, we
propose a gradual source domain expansion (GSDE) algorithm. GSDE trains the UDA
task several times from scratch, each time reinitializing the network weights,
but each time expands the source dataset with target data. In particular, the
highest-scoring target data of the previous run are employed as pseudo-source
samples with their respective pseudo-label. Using this strategy, the
pseudo-source samples induce knowledge extracted from the previous run directly
from the start of the new training. This helps align the two domains better,
especially in the early training epochs. In this study, we first introduce a
strong baseline network and apply our GSDE strategy to it. We conduct
experiments and ablation studies on three benchmarks (Office-31, OfficeHome,
and DomainNet) and outperform state-of-the-art methods. We further show that
the proposed GSDE strategy can improve the accuracy of a variety of different
state-of-the-art UDA approaches.
