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

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.
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.
DELAUNAY: a dataset of abstract art for psychophysical and machine learning research
http://arxiv.org/abs/2201.12123v1