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

arXiv: https://arxiv.org/abs/2201.12123 · HTML: https://mlsanity.com/paper/2201.12123/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Generalisation in humans and deep neural networks](https://mlsanity.com/paper/1808.08750/) (2018)
- [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017)
- [Dendritic cortical microcircuits approximate the backpropagation algorithm](https://mlsanity.com/paper/1810.11393/) (2018)
- [Towards deep learning with segregated dendrites](https://mlsanity.com/paper/1610.00161/) (2016)
- [Deep Nets: What have they ever done for Vision?](https://mlsanity.com/paper/1805.04025/) (2018)
- [Car Type Recognition with Deep Neural Networks](https://mlsanity.com/paper/1602.07125/) (2016)

## Built on by (most cited first, 5 of 5)

- [Is Pre-training Truly Better Than Meta-Learning?](https://mlsanity.com/paper/2306.13841/) (2023)
- [Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting](https://mlsanity.com/paper/2509.26455/) (2025)
- [Vectorized Region Based Brush Strokes for Artistic Rendering](https://mlsanity.com/paper/2506.09969/) (2025)
- [NSYNC: Negative Synthetic Image Generation for Contrastive Training to Improve Stylized Text-To-Image Translation](https://mlsanity.com/paper/2511.01517/) (2025)
- [Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art](https://mlsanity.com/paper/2608.28339/) (2026)

## Abstract (arXiv)

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.