论文标题
Delaunay：用于心理物理和机器学习研究的抽象艺术数据集
DELAUNAY: a dataset of abstract art for psychophysical and machine learning research
论文作者
论文摘要
图像数据集通常用于心理物理实验和机器学习研究中。大多数公开可用的数据集由现实和自然对象的图像组成。但是，尽管典型的机器学习模型缺乏有关自然物体的任何特定领域知识，但人类可以利用先前的经验来获得此类数据，从而在人工和自然学习之间进行比较。在这里，我们介绍了Delaunay，这是一个抽象绘画的数据集和由艺术家名字标记的非物质艺术对象。该数据集提供了自然图像和人造模式之间的中间立场，因此可以在各种情况下使用，例如研究人类和人工神经网络的样本效率。最后，我们在Delaunay培训了一个现成的卷积神经网络，重点介绍了其一些有趣的功能。
Image datasets are commonly used in psychophysical experiments and in machine learning research. Most publicly available datasets are comprised of images of realistic and natural objects. However, while typical machine learning models lack any domain specific knowledge about natural objects, humans can leverage prior experience for such data, making comparisons between artificial and natural learning challenging. Here, we introduce DELAUNAY, a dataset of abstract paintings and non-figurative art objects labelled by the artists' names. This dataset provides a middle ground between natural images and artificial patterns and can thus be used in a variety of contexts, for example to investigate the sample efficiency of humans and artificial neural networks. Finally, we train an off-the-shelf convolutional neural network on DELAUNAY, highlighting several of its intriguing features.
