• Corpus ID: 220056269

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

@inproceedings{Lin2020InfoGANCRAM,
  title={InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs},
  author={Zinan Lin and Kiran Koshy Thekumparampil and Giulia C. Fanti and Sewoong Oh},
  booktitle={ICML},
  year={2020}
}
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… 
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