  Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as
it allows to bridge different visual domains enabling robust performances in
the real world. To date, all proposed approaches rely on human expertise to
manually adapt a given UDA method (e.g. DANN) to a specific backbone
architecture (e.g. ResNet). This dependency on handcrafted designs limits the
applicability of a given approach in time, as old methods need to be constantly
adapted to novel backbones.
  Existing Neural Architecture Search (NAS) approaches cannot be directly
applied to mitigate this issue, as they rely on labels that are not available
in the UDA setting. Furthermore, most NAS methods search for full
architectures, which precludes the use of pre-trained models, essential in a
vast range of UDA settings for reaching SOTA results. To the best of our
knowledge, no prior work has addressed these aspects in the context of NAS for
UDA. Here we tackle both aspects with an Adversarial Branch Architecture Search
for UDA (ABAS): i. we address the lack of target labels by a novel data-driven
ensemble approach for model selection; and ii. we search for an auxiliary
adversarial branch, attached to a pre-trained backbone, which drives the domain
alignment.
  We extensively validate ABAS to improve two modern UDA techniques, DANN and
ALDA, on three standard visual recognition datasets (Office31, Office-Home and
PACS). In all cases, ABAS robustly finds the adversarial branch architectures
and parameters which yield best performances.
