# Top-Down Deep Clustering with Multi-generator GANs

arXiv: https://arxiv.org/abs/2112.03398 · HTML: https://mlsanity.com/paper/2112.03398/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization](https://mlsanity.com/paper/1704.06327/) (2017)
- [Deep Subspace Clustering Networks](https://mlsanity.com/paper/1709.02508/) (2017)
- [SpectralNet: Spectral Clustering using Deep Neural Networks](https://mlsanity.com/paper/1801.01587/) (2018)
- [Deep Comprehensive Correlation Mining for Image Clustering](https://mlsanity.com/paper/1904.06925/) (2019)
- [ClusterGAN : Latent Space Clustering in Generative Adversarial Networks](https://mlsanity.com/paper/1809.03627/) (2018)
- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [Amortised MAP Inference for Image Super-resolution](https://mlsanity.com/paper/1610.04490/) (2016)
- [Multi-Agent Diverse Generative Adversarial Networks](https://mlsanity.com/paper/1704.02906/) (2017)
- [On Stabilizing Generative Adversarial Training with Noise](https://mlsanity.com/paper/1906.04612/) (2019)
- [Neural Collaborative Subspace Clustering](https://mlsanity.com/paper/1904.10596/) (2019)
- [Learning Markov Clustering Networks for Scene Text Detection](https://mlsanity.com/paper/1805.08365/) (2018)
- [Scalable Deep k-Subspace Clustering](https://mlsanity.com/paper/1811.01045/) (2018)
- [Deep Image Clustering with Category-Style Representation](https://mlsanity.com/paper/2007.10004/) (2020)
- [Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures](https://mlsanity.com/paper/1811.08010/) (2018)
- [GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions](https://mlsanity.com/paper/1908.03919/) (2019)
- [Double cycle-consistent generative adversarial network for unsupervised conditional generation](https://mlsanity.com/paper/1911.05210/) (2019)

## Built on by (most cited first, 6 of 6)

- [GLCC: A General Framework for Graph-Level Clustering](https://mlsanity.com/paper/2210.11879/) (2022)
- [K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs](https://mlsanity.com/paper/2302.11996/) (2023)
- [TDEC: Deep Embedded Image Clustering with Transformer and Distribution Information](https://mlsanity.com/paper/2603.26746/) (2026)
- [Deep Image Clustering Based on Curriculum Learning and Density Information](https://mlsanity.com/paper/2604.03306/) (2026)
- [A Tutorial on Discriminative Clustering and Mutual Information](https://mlsanity.com/paper/2505.04484/) (2025)
- [Clustering High-dimensional Data: Balancing Abstraction and Representation Tutorial at AAAI 2026](https://mlsanity.com/paper/2601.11160/) (2026)

## Abstract (arXiv)

Deep clustering (DC) leverages the representation power of deep architectures to learn embedding spaces that are optimal for cluster analysis. This approach filters out low-level information irrelevant for clustering and has proven remarkably successful for high dimensional data spaces. Some DC methods employ Generative Adversarial Networks (GANs), motivated by the powerful latent representations these models are able to learn implicitly. In this work, we propose HC-MGAN, a new technique based on GANs with multiple generators (MGANs), which have not been explored for clustering. Our method is inspired by the observation that each generator of a MGAN tends to generate data that correlates with a sub-region of the real data distribution. We use this clustered generation to train a classifier for inferring from which generator a given image came from, thus providing a semantically meaningful clustering for the real distribution. Additionally, we design our method so that it is performed in a top-down hierarchical clustering tree, thus proposing the first hierarchical DC method, to the best of our knowledge. We conduct several experiments to evaluate the proposed method against recent DC methods, obtaining competitive results. Last, we perform an exploratory analysis of the hierarchical clustering tree that highlights how accurately it organizes the data in a hierarchy of semantically coherent patterns.