# Deep Fair Discriminative Clustering

arXiv: https://arxiv.org/abs/2105.14146 · HTML: https://mlsanity.com/paper/2105.14146/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 21 of 21)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [A Semantic Loss Function for Deep Learning with Symbolic Knowledge](https://mlsanity.com/paper/1711.11157/) (2017)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Learning Discrete Representations via Information Maximizing Self-Augmented Training](https://mlsanity.com/paper/1702.08720/) (2017)
- [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://mlsanity.com/paper/1906.06532/) (2019)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [SpectralNet: Spectral Clustering using Deep Neural Networks](https://mlsanity.com/paper/1801.01587/) (2018)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [Fair k-Center Clustering for Data Summarization](https://mlsanity.com/paper/1901.08628/) (2019)
- [Individual Fairness for k-Clustering](https://mlsanity.com/paper/2002.06742/) (2020)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [A Pairwise Fair and Community-preserving Approach to k-Center Clustering](https://mlsanity.com/paper/2007.07384/) (2020)
- [Deep Continuous Clustering](https://mlsanity.com/paper/1803.01449/) (2018)
- [Towards Fair Deep Clustering With Multi-State Protected Variables](https://mlsanity.com/paper/1901.10053/) (2019)
- [Deep Discriminative Latent Space for Clustering](https://mlsanity.com/paper/1805.10795/) (2018)

## Built on by (most cited first, 10 of 10)

- [Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric](https://mlsanity.com/paper/2209.12396/) (2022)
- [Robust Fair Clustering: A Novel Fairness Attack and Defense Framework](https://mlsanity.com/paper/2210.01953/) (2022)
- [Exploring a Principled Framework for Deep Subspace Clustering](https://mlsanity.com/paper/2503.17288/) (2025)
- [Efficient Algorithms For Fair Clustering with a New Fairness Notion](https://mlsanity.com/paper/2109.00708/) (2021)
- [Holistic Survey of Privacy and Fairness in Machine Learning](https://mlsanity.com/paper/2307.15838/) (2023)
- [Adversarial Fair Multi-View Clustering](https://mlsanity.com/paper/2508.04071/) (2025)
- [From Discrete to Continuous: Deep Fair Clustering With Transferable Representations](https://mlsanity.com/paper/2403.16201/) (2024)
- [Interpretable Fair Clustering](https://mlsanity.com/paper/2511.21109/) (2025)
- [UniFair: A unified fair clustering approach based on separation and compactness](https://mlsanity.com/paper/2606.04777/) (2026)
- [Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancy](https://mlsanity.com/paper/2609.25811/) (2026)

## Abstract (arXiv)

Deep clustering has the potential to learn a strong representation and hence better clustering performance compared to traditional clustering methods such as k-means and spectral clustering. However, this strong representation learning ability may make the clustering unfair by discovering surrogates for protected information which we empirically show in our experiments. In this work, we study a general notion of group-level fairness for both binary and multi-state protected status variables (PSVs). We begin by formulating the group-level fairness problem as an integer linear programming formulation whose totally unimodular constraint matrix means it can be efficiently solved via linear programming. We then show how to inject this solver into a discriminative deep clustering backbone and hence propose a refinement learning algorithm to combine the clustering goal with the fairness objective to learn fair clusters adaptively. Experimental results on real-world datasets demonstrate that our model consistently outperforms state-of-the-art fair clustering algorithms. Our framework shows promising results for novel clustering tasks including flexible fairness constraints, multi-state PSVs and predictive clustering.