  Generative adversarial networks (GANs) learn a deep generative model that is
able to synthesise novel, high-dimensional data samples. New data samples are
synthesised by passing latent samples, drawn from a chosen prior distribution,
through the generative model. Once trained, the latent space exhibits
interesting properties, that may be useful for down stream tasks such as
classification or retrieval. Unfortunately, GANs do not offer an "inverse
model", a mapping from data space back to latent space, making it difficult to
infer a latent representation for a given data sample. In this paper, we
introduce a technique, inversion, to project data samples, specifically images,
to the latent space using a pre-trained GAN. Using our proposed inversion
technique, we are able to identify which attributes of a dataset a trained GAN
is able to model and quantify GAN performance, based on a reconstruction loss.
We demonstrate how our proposed inversion technique may be used to
quantitatively compare performance of various GAN models trained on three image
datasets. We provide code for all of our experiments,
https://github.com/ToniCreswell/InvertingGAN.
