  Unsupervised domain adaption (UDA) aims to adapt models learned from a
well-annotated source domain to a target domain, where only unlabeled samples
are given. Current UDA approaches learn domain-invariant features by aligning
source and target feature spaces. Such alignments are imposed by constraints
such as statistical discrepancy minimization or adversarial training. However,
these constraints could lead to the distortion of semantic feature structures
and loss of class discriminability. In this paper, we introduce a novel prompt
learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL).
In contrast to prior works, our approach makes use of pre-trained
vision-language models and optimizes only very few parameters. The main idea is
to embed domain information into prompts, a form of representations generated
from natural language, which is then used to perform classification. This
domain information is shared only by images from the same domain, thereby
dynamically adapting the classifier according to each domain. By adopting this
paradigm, we show that our model not only outperforms previous methods on
several cross-domain benchmarks but also is very efficient to train and easy to
implement.
