# DivClust: Controlling Diversity in Deep Clustering

arXiv: https://arxiv.org/abs/2304.01042 · HTML: https://mlsanity.com/paper/2304.01042/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 86 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 30 of 35)

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [Deep Subspace Clustering Networks](https://mlsanity.com/paper/1709.02508/) (2017)
- [Deep Comprehensive Correlation Mining for Image Clustering](https://mlsanity.com/paper/1904.06925/) (2019)
- [Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation](https://mlsanity.com/paper/2106.00131/) (2021)
- [Graph Contrastive Clustering](https://mlsanity.com/paper/2104.01429/) (2021)
- [Learning Representation for Clustering via Prototype Scattering and Positive Sampling](https://mlsanity.com/paper/2111.11821/) (2021)
- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [HCSC: Hierarchical Contrastive Selective Coding](https://mlsanity.com/paper/2202.00455/) (2022)
- [MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering](https://mlsanity.com/paper/2105.01899/) (2021)
- [You Never Cluster Alone](https://mlsanity.com/paper/2106.01908/) (2021)
- [GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering](https://mlsanity.com/paper/2002.11863/) (2020)
- [Deep Discriminative Clustering Analysis](https://mlsanity.com/paper/1905.01681/) (2019)
- [Locally Weighted Ensemble Clustering](https://mlsanity.com/paper/1605.05011/) (2016)
- [Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities](https://mlsanity.com/paper/1810.12544/) (2018)
- [Consensus Clustering With Unsupervised Representation Learning](https://mlsanity.com/paper/2010.01245/) (2020)
- [Deep Clustering by Semantic Contrastive Learning](https://mlsanity.com/paper/2103.02662/) (2021)
- [Deep Image Clustering with Category-Style Representation](https://mlsanity.com/paper/2007.10004/) (2020)
- [Multi-View Multiple Clusterings using Deep Matrix Factorization](https://mlsanity.com/paper/1911.11396/) (2019)
- [Multi-View Multiple Clustering](https://mlsanity.com/paper/1905.05053/) (2019)
- [Representation Learning for Clustering via Building Consensus](https://mlsanity.com/paper/2105.01289/) (2021)
- [Ensemble Clustering via Co-association Matrix Self-enhancement](https://mlsanity.com/paper/2205.05937/) (2022)

## Built on by (most cited first, 18 of 18)

- [Image Clustering Conditioned on Text Criteria](https://mlsanity.com/paper/2310.18297/) (2023)
- [P2OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering](https://mlsanity.com/paper/2401.09266/) (2024)
- [Contextually Affinitive Neighborhood Refinery for Deep Clustering](https://mlsanity.com/paper/2312.07806/) (2023)
- [On the Provable Importance of Gradients for Language-Assisted Image Clustering](https://mlsanity.com/paper/2510.16335/) (2025)
- [Organizing Unstructured Image Collections using Natural Language](https://mlsanity.com/paper/2410.05217/) (2024)
- [Towards Calibrated Deep Clustering Network](https://mlsanity.com/paper/2403.02998/) (2024)
- [Open Ad-hoc Categorization with Contextualized Feature Learning](https://mlsanity.com/paper/2512.16202/) (2025)
- [SP2OT: Semantic-Regularized Progressive Partial Optimal Transport for Imbalanced Clustering](https://mlsanity.com/paper/2404.03446/) (2024)
- [Bootstrap Deep Spectral Clustering with Optimal Transport](https://mlsanity.com/paper/2508.04200/) (2025)
- [Delving into Spectral Clustering with Vision-Language Representations](https://mlsanity.com/paper/2602.09586/) (2026)
- [Generalization Performance of Ensemble Clustering: From Theory to Algorithm](https://mlsanity.com/paper/2506.02053/) (2025)
- [CLUBench: A Clustering Benchmark](https://mlsanity.com/paper/2605.29933/) (2026)
- [Historical Printed Ornaments: Dataset and Tasks](https://mlsanity.com/paper/2408.08633/) (2024)
- [Deep Clustering via Gradual Community Detection](https://mlsanity.com/paper/2501.02036/) (2025)
- [Interpretable Text-Guided Image Clustering via Iterative Search](https://mlsanity.com/paper/2506.12514/) (2025)
- [Hierarchical Textual Knowledge for Enhanced Image Clustering](https://mlsanity.com/paper/2604.11144/) (2026)
- [Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering](https://mlsanity.com/paper/2605.12961/) (2026)
- [Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent](https://mlsanity.com/paper/2606.24094/) (2026)

## Abstract (arXiv)

Clustering has been a major research topic in the field of machine learning, one to which Deep Learning has recently been applied with significant success. However, an aspect of clustering that is not addressed by existing deep clustering methods, is that of efficiently producing multiple, diverse partitionings for a given dataset. This is particularly important, as a diverse set of base clusterings are necessary for consensus clustering, which has been found to produce better and more robust results than relying on a single clustering. To address this gap, we propose DivClust, a diversity controlling loss that can be incorporated into existing deep clustering frameworks to produce multiple clusterings with the desired degree of diversity. We conduct experiments with multiple datasets and deep clustering frameworks and show that: a) our method effectively controls diversity across frameworks and datasets with very small additional computational cost, b) the sets of clusterings learned by DivClust include solutions that significantly outperform single-clustering baselines, and c) using an off-the-shelf consensus clustering algorithm, DivClust produces consensus clustering solutions that consistently outperform single-clustering baselines, effectively improving the performance of the base deep clustering framework.