Corpus ID: 236447844

Improving ClusterGAN Using Self-AugmentedInformation Maximization of Disentangling LatentSpaces

  title={Improving ClusterGAN Using Self-AugmentedInformation Maximization of Disentangling LatentSpaces},
  author={Tanmoy Dam and Sreenatha G. Anavatti and Hussein A. Abbass},
I. ABSTRACT The Latent Space Clustering in Generative Adversarial Networks (ClusterGAN) method has been successful with high dimensional data. However, the method assumes uniformly distributed priors during the generation of modes, which is a restrictive assumption in real-world data and cause loss of diversity in the generated modes. In this paper, we propose self-augmentation information maximization improved ClusterGAN (SIMI-ClusterGAN) to learn the distinctive priors from the data. The… Expand


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