• DOI:10.1609/aaai.v33i01.33014610
Corpus ID: 52188737

ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

@article{Mukherjee2019ClusterGANL,
  title={ClusterGAN : Latent Space Clustering in Generative Adversarial Networks},
  author={S. Mukherjee and Himanshu Asnani and Eugene Lin and S. Kannan},
  journal={ArXiv},
  year={2019},
  volume={abs/1809.03627}
}

• S. Mukherjee, Himanshu Asnani, +1 author S. Kannan
• Published 2019
• Computer Science, Mathematics
• ArXiv

Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. [...] Key Method 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…Expand

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