DELAUNAY：用于心理物理学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
摘要：
图像数据集通常用于心理物理实验和机器学习研究。大多数公开可用的数据集由真实和自然物体的图像组成。然而，虽然典型的机器学习模型缺乏关于自然对象的任何特定领域的知识，但人类可以利用这些数据的先前经验，使得人工学习和自然学习之间的比较具有挑战性。在这里，我们介绍DELAUNAY，这是一个由艺术家名字标记的抽象绘画和非具象艺术品的数据集。该数据集提供了自然图像和人工模式之间的中间地带，因此可以用于各种背景，例如研究人类和人工神经网络的样本效率。最后，我们在DELAUNAY上训练了一个现成的卷积神经网络，突出了它的几个有趣的特性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
