  From classifying handwritten digits to generating strings of text, the
datasets which have received long-time focus from the machine learning
community vary greatly in their subject matter. This has motivated a renewed
interest in building datasets which are socially and culturally relevant, so
that algorithmic research may have a more direct and immediate impact on
society. One such area is in history and the humanities, where better and
relevant machine learning models can accelerate research across various fields.
To this end, newly released benchmarks and models have been proposed for
transcribing historical Japanese cursive writing, yet for the field as a whole
using machine learning for historical Japanese artworks still remains largely
uncharted. To bridge this gap, in this work we propose a new dataset KaoKore
which consists of faces extracted from pre-modern Japanese artwork. We
demonstrate its value as both a dataset for image classification as well as a
creative and artistic dataset, which we explore using generative models.
Dataset available at https://github.com/rois-codh/kaokore
