DELAUNAY: a dataset of abstract art for psychophysical and machine learning research

TL;DR

DELAUNAY is a novel dataset of abstract art designed to bridge the gap between natural images and artificial patterns, facilitating research in psychophysics and machine learning.

Contribution

The paper introduces DELAUNAY, a new dataset of abstract paintings with artist labels, enabling studies on sample efficiency and comparison between human and machine learning.

Findings

CNN trained on DELAUNAY shows intriguing features

Dataset useful for investigating human and AI learning

Bridges gap between natural and artificial image datasets

Abstract

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.