{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/clustergan-latent-space-clustering-in","title":"ClusterGAN : Latent Space Clustering in Generative Adversarial Networks","arxiv_id":"1809.03627","date":"2018-09-10","proceeding":null,"authors":["Sudipto Mukherjee","Himanshu Asnani","Eugene Lin","Sreeram Kannan"],"abstract":"Generative Adversarial networks (GANs) have obtained remarkable success in\nmany unsupervised learning tasks and unarguably, clustering is an important\nunsupervised learning problem. While one can potentially exploit the\nlatent-space back-projection in GANs to cluster, we demonstrate that the\ncluster structure is not retained in the GAN latent space.\n  In this paper, we propose ClusterGAN as a new mechanism for clustering using\nGANs. By sampling latent variables from a mixture of one-hot encoded variables\nand continuous latent variables, coupled with an inverse network (which\nprojects the data to the latent space) trained jointly with a clustering\nspecific loss, we are able to achieve clustering in the latent space. Our\nresults show a remarkable phenomenon that GANs can preserve latent space\ninterpolation across categories, even though the discriminator is never exposed\nto such vectors. We compare our results with various clustering baselines and\ndemonstrate superior performance on both synthetic and real datasets.","url_abs":"http://arxiv.org/abs/1809.03627v2","url_pdf":"http://arxiv.org/pdf/1809.03627v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"clustergan-latent-space-clustering-in","repo_url":"https://github.com/adriangrepo/caribbean_disaster_risk","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"clustergan-latent-space-clustering-in","repo_url":"https://github.com/eriklindernoren/PyTorch-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"clustergan-latent-space-clustering-in","repo_url":"https://github.com/hustwutao/Deep-Clustering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"clustergan-latent-space-clustering-in","repo_url":"https://github.com/lvyufeng/MindSpore-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"clustergan-latent-space-clustering-in","repo_url":"https://github.com/sudiptodip15/ClusterGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"clustergan-latent-space-clustering-in","repo_url":"https://github.com/zhampel/clusterGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"clustergan-latent-space-clustering-in","repo_url":"https://github.com/BY571/GANs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.03627","atlas_url":"https://app.syntology.ai/?focus=1809.03627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.03627"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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