  Archaeology is an intriguing domain for computer vision. It suffers not only
from shortage in (labeled) data, but also from highly-challenging data, which
is often extremely abraded and damaged. This paper proposes a novel
semi-supervised model for classification and retrieval of images of
archaeological artifacts. This model utilizes unique data that exists in the
domain -- manual drawings made by special artists. These are used during
training to implicitly transfer the domain knowledge from the drawings to their
corresponding images, improving their classification results. We show that
while learning how to classify, our model also learns how to generate drawings
of the artifacts, an important documentation task, which is currently performed
manually. Last but not least, we collected a new dataset of stamp-seals of the
Southern Levant. Our code and dataset are publicly available.
