  Domain adaptation enhances generalizability of a model across domains with
domain shifts. Most research effort has been spent on Unsupervised Domain
Adaption (UDA) which trains a model jointly with labeled source data and
unlabeled target data. This paper studies how much it can help address domain
shifts if we further have a few target samples (e.g., one sample per class)
labeled. This is the so-called semi-supervised domain adaptation (SSDA) problem
and the few labeled target samples are termed as ``landmarks''. To explore the
full potential of landmarks, we incorporate a prototypical alignment (PA)
module which calculates a target prototype for each class from the landmarks;
source samples are then aligned with the target prototype from the same class.
To further alleviate label scarcity, we propose a data augmentation based
solution. Specifically, we severely perturb the labeled images, making PA
non-trivial to achieve and thus promoting model generalizability. Moreover, we
apply consistency learning on unlabeled target images, by perturbing each image
with light transformations and strong transformations. Then, the strongly
perturbed image can enjoy ``supervised-like'' training using the pseudo label
inferred from the lightly perturbed one. Experiments show that the proposed
method, though simple, reaches significant performance gains over
state-of-the-art methods, and enjoys the flexibility of being able to serve as
a plug-and-play component to various existing UDA methods and improve
adaptation performance with landmarks provided. Our code is available at
\url{https://github.com/kailigo/pacl}.
