2018/12/03 by Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto +3 · 2 voices · 3 citations
Computer Science · Mathematics · #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.20676/00000341
To appear at Neural Information Processing Systems 2018 Workshop on Machine Learning for Creativity and Design
arxiv created 2018/12/03 · arxiv published 2018/12/03 · arxiv updated 2018/12/06
Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Dataset available at https://github.com/rois-codh/kmnist