Paper

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

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... (read more)

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