# Capturing the Flow of Art History

arXiv: https://arxiv.org/abs/2212.03421 · HTML: https://mlsanity.com/paper/2212.03421/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [The Shape of Art History in the Eyes of the Machine](https://mlsanity.com/paper/1801.07729/) (2018)
- [Fixing the train-test resolution discrepancy: FixEfficientNet](https://mlsanity.com/paper/2003.08237/) (2020)

## Abstract (arXiv)

Do we really understand how machine classifies art styles? Historically, art is perceived and interpreted by human eyes and there are always controversial discussions over how people identify and understand art. Historians and general public tend to interpret the subject matter of art through the context of history and social factors. Style, however, is different from subject matter. Given the fact that Style does not correspond to the existence of certain objects in the painting and is mainly related to the form and can be correlated with features at different levels.(Ahmed Elgammal et al. 2018), which makes the identification and classification of the characteristics artwork's style and the "transition" - how it flows and evolves - remains as a challenge for both human and machine. In this work, a series of state-of-art neural networks and manifold learning algorithms are explored to unveil this intriguing topic: How does machine capture and interpret the flow of Art History?