mlsanityData to 14 Oct 2026

ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

arXiv 1809.03627 ↗First posted 10 Sep 2018cs.LG stat.MLcited by 75

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Abstract (arXiv)

Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the GAN latent space. In this paper, we propose ClusterGAN as a new mechanism for clustering using GANs. By sampling latent variables from a mixture of one-hot encoded variables and continuous latent variables, coupled with an inverse network (which projects the data to the latent space) trained jointly with a clustering specific loss, we are able to achieve clustering in the latent space. Our results show a remarkable phenomenon that GANs can preserve latent space interpolation across categories, even though the discriminator is never exposed to such vectors. We compare our results with various clustering baselines and demonstrate superior performance on both synthetic and real datasets.

Key facts. Cited by 75 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Sep 2018.

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Citing papers per year: 2021: 24; 2022: 14; 2023: 10; 2024: 7; 2025: 12; 2026: 8.

75 papers.

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  1. 2022Deep Clustering: A Comprehensive Surveycited by 78
  2. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  3. 2021Learning Disentangled Representations in the Imaging Domaincited by 46
  4. 2021MiCE: Mixture of Contrastive Experts for Unsupervised Image Clusteringcited by 42
  5. 2021You Never Cluster Alonecited by 38
  6. 2021Multi-Facet Clustering Variational Autoencoderscited by 28
  7. 2021Learning a Self-Expressive Network for Subspace Clusteringcited by 24
  8. 2022GLCC: A General Framework for Graph-Level Clusteringcited by 21
  9. 2022Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metriccited by 17
  10. 2021Unsupervised Two-Stage Anomaly Detectioncited by 12
  11. 2022Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neurosciencecited by 11
  12. 2022Secure Federated Clusteringcited by 10
38 more
  1. 2024A Survey on Deep Clustering: From the Prior Perspectivecited by 9
  2. 2021Selective Pseudo-label Clusteringcited by 8
  3. 2023Redundancy-Free Self-Supervised Relational Learning for Graph Clusteringcited by 8
  4. 2025On the Provable Importance of Gradients for Language-Assisted Image Clusteringcited by 8
  5. 2021Unsupervised anomaly detection in digital pathology using GANscited by 7
  6. 2021DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editingcited by 7
  7. 2022Unsupervised Learning of Structured Representations via Closed-Loop Transcriptioncited by 7
  8. 2024Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusterscited by 7
  9. 2021Top-Down Deep Clustering with Multi-generator GANscited by 6
  10. 2022Enhancing cluster analysis via topological manifold learningcited by 6
  11. 2023K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairscited by 6
  12. 2021Encoding Event-Based Data With a Hybrid SNN Guided Variational Auto-encoder in Neuromorphic Hardwarecited by 5
  13. 2021Incompatibility Clustering as a Defense Against Backdoor Poisoning Attackscited by 5
  14. 2021A Mechanism for Producing Aligned Latent Spaces with Autoencoderscited by 5
  15. 2023MCoCo: Multi-level Consistency Collaborative Multi-view Clusteringcited by 5
  16. 2023Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCEcited by 5
  17. 2023FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative Modelscited by 5
  18. 2022Multi-level Latent Space Structuring for Generative Controlcited by 4
  19. 2024Towards Action Hijacking of Large Language Model-based Agentcited by 4
  20. 2021Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Imagescited by 3
  21. 2021Cluster-guided Image Synthesis with Unconditional Modelscited by 3
  22. 2022Unsupervised Deep Discriminant Analysis Based Clusteringcited by 3
  23. 2022Federated clustering with GAN-based data synthesiscited by 3
  24. 2025Exploring the latent space of diffusion models directly through singular value decompositioncited by 3
  25. 2025Quantum latent distributions in deep generative modelscited by 3
  26. 2021CoDeGAN: Contrastive Disentanglement for Generative Adversarial Networkcited by 2
  27. 2021DPR-CAE: Capsule Autoencoder with Dynamic Part Representation for Image Parsingcited by 2
  28. 2021Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spacescited by 2
  29. 2023ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Modelscited by 2
  30. 2024Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineeringcited by 2
  31. 2026Delving into Spectral Clustering with Vision-Language Representationscited by 2
  32. 2021Stein Latent Optimization for Generative Adversarial Networkscited by 1
  33. 2021Unsupervised Image Generation with Infinite Generative Adversarial Networkscited by 1
  34. 2022Deep clustering with fusion autoencodercited by 1
  35. 2023Density peak clustering using tensor networkcited by 1
  36. 2023Kernel Density Matrices for Probabilistic Deep Learningcited by 1
  37. 2023Controlling the Output of a Generative Model by Latent Feature Vector Shiftingcited by 1
  38. 2024Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Spacecited by 1

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