# The persistence of painting styles

arXiv: https://arxiv.org/abs/2511.16695 · HTML: https://mlsanity.com/paper/2511.16695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [The Shape of Art History in the Eyes of the Machine](https://mlsanity.com/paper/1801.07729/) (2018)
- [Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models](https://mlsanity.com/paper/2201.01819/) (2022)
- [Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations](https://mlsanity.com/paper/2409.08245/) (2024)
- [DONUT -- Creation, Development, and Opportunities of a Database](https://mlsanity.com/paper/2304.12417/) (2023)

## Abstract (arXiv)

Art is a deeply personal and expressive medium, where each artist brings their own style, technique, and cultural background into their work. Traditionally, identifying artistic styles has been the job of art historians or critics, relying on visual intuition and experience. However, with the advancement of mathematical tools, we can explore art through more structured lens. In this work, we show how persistent homology (PH), a method from topological data analysis, provides objective and interpretable insights on artistic styles. We show how PH can, with statistical certainty, differentiate between artists, both from different artistic currents and from the same one, and distinguish images of an artist from an AI-generated image in the artist's style.