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KaoKore: The Pre-modern Japanese Art Facial Expression Dataset

Chikahiko Suzuki
Tarin Clanuwat
Mikel Bober-Irizar
Alex Lamb
Asanobu Kitamoto
Proceedings of the 11th International Conference on Computational Creatvity (2020), pp. 415-422


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 creating datasets which are socially and culturally relevant, so that algorithmic research may have a more direct and immediate impact. One such area is in history and the humanities, where better machine learning models could help to accelerate research. Along this line, newly created benchmarks and models have been proposed for historical Japanese cursive writing. At the same time, using machine learning for historical Japanese artworks has remained largely uncharted. In this work we propose a new dataset Kaokore which consists of faces from Pre-modern Japanese arts. We demonstrate its value as both a classification dataset as well as a creative and artistic dataset, which we explore using generative models.

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