Generalized Latent Variable Recovery for Generative Adversarial Networks
The Generator of a Generative Adversarial Network (GAN) is trained to transform latent vectors drawn from a prior distribution into realistic looking photos. These latent vectors have been shown to encode information about the content of their corresponding images. Projecting input images onto the latent space of a GAN is non-trivial, but previous work has successfully performed this task for latent spaces with a uniform prior. We extend these techniques to latent spaces with a Gaussian prior, and demonstrate our technique's effectiveness.
See our paper for details.
Our GAN was trained on food photos from the Yelp dataset and uses the DCGAN architecture in PyTorch.