Corpus ID: 220056269

InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs

@article{Lin2019InfoGANCRAM,
  title={InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs},
  author={Z. Lin and Kiran Koshy Thekumparampil and Giulia Fanti and Sewoong Oh},
  journal={arXiv: Learning},
  year={2019}
}

Z. Lin, Kiran Koshy Thekumparampil, +1 author Sewoong Oh

Published 2019

Computer Science, Mathematics

arXiv: Learning

Disentangled generative models map a latent code vector to a target space, while enforcing that a subset of the learned latent codes are interpretable and associated with distinct properties of the target distribution. Recent advances have been dominated by Variational AutoEncoder (VAE)-based methods, while training disentangled generative adversarial networks (GANs) remains challenging. In this work, we show that the dominant challenges facing disentangled GANs can be mitigated through the use… CONTINUE READING

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