# A deep learning approach to clustering visual arts

arXiv: https://arxiv.org/abs/2106.06234 · HTML: https://mlsanity.com/paper/2106.06234/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 59 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [Weakly Supervised Object Detection in Artworks](https://mlsanity.com/paper/1810.02569/) (2018)
- [Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning](https://mlsanity.com/paper/1903.02678/) (2019)
- [Detecting People in Artwork with CNNs](https://mlsanity.com/paper/1610.08871/) (2016)
- [Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation](https://mlsanity.com/paper/1811.10666/) (2018)
- [OmniArt: Multi-task Deep Learning for Artistic Data Analysis](https://mlsanity.com/paper/1708.00684/) (2017)
- [GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph](https://mlsanity.com/paper/2105.11852/) (2021)
- [The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs](https://mlsanity.com/paper/1505.00110/) (2015)
- [Toward Automated Discovery of Artistic Influence](https://mlsanity.com/paper/1408.3218/) (2014)
- [Visual link retrieval and knowledge discovery in painting datasets](https://mlsanity.com/paper/2003.08476/) (2020)
- [Deep convolutional embedding for digitized painting clustering](https://mlsanity.com/paper/2003.08597/) (2020)

## Built on by (most cited first, 4 of 4)

- [ArtSeek: Deep artwork understanding via multimodal in-context reasoning and late interaction retrieval](https://mlsanity.com/paper/2507.21917/) (2025)
- [Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations](https://mlsanity.com/paper/2409.08245/) (2024)
- [Adaptive Discriminative Regularization for Visual Classification](https://mlsanity.com/paper/2203.00833/) (2022)
- [Curating art exhibitions using machine learning](https://mlsanity.com/paper/2506.19813/) (2025)

## Abstract (arXiv)

Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns based on domain knowledge and visual perception is extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, in this paper we propose DELIUS: a DEep learning approach to cLustering vIsUal artS. The method uses a pre-trained convolutional network to extract features and then feeds these features into a deep embedded clustering model, where the task of mapping the input data to a latent space is jointly optimized with the task of finding a set of cluster centroids in this latent space. Quantitative and qualitative experimental results show the effectiveness of the proposed method. DELIUS can be useful for several tasks related to art analysis, in particular visual link retrieval and historical knowledge discovery in painting datasets.