论文标题
Kaokore：前现代的日本艺术面部表情数据集
KaoKore: A Pre-modern Japanese Art Facial Expression Dataset
论文作者
论文摘要
从分类手写数字到生成文本字符串，从机器学习社区长期重点的数据集在其主题方面差异很大。这激发了人们对建立在社会和文化上相关的数据集的重新兴趣，因此算法研究可能会对社会产生更直接和直接的影响。这样一个领域是历史和人文科学，在那里更好，相关的机器学习模型可以加速各个领域的研究。为此，已经提出了新发布的基准和模型来转录日本历史的草书写作，但是对于整个领域，使用机器学习的历史日本艺术品仍在很大程度上尚未了解。为了弥合这一差距，在这项工作中，我们提出了一个新的数据集kaokore，该数据集由从日本前艺术品中提取的面孔组成。我们证明了它作为图像分类的数据集以及创意和艺术数据集的价值，我们使用生成模型进行了探索。数据集可从https://github.com/rois-codh/kaokore获得
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
