BibTex format
@article{Creswell:2018,
author = {Creswell, A and Bharath, AA},
title = {Inverting The Generator Of A Generative Adversarial Network (II)},
url = {http://arxiv.org/abs/1802.05701v1},
year = {2018}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - JOUR
AB - Generative adversarial networks (GANs) learn a deep generative model that isable to synthesise novel, high-dimensional data samples. New data samples aresynthesised by passing latent samples, drawn from a chosen prior distribution,through the generative model. Once trained, the latent space exhibitsinteresting properties, that may be useful for down stream tasks such asclassification or retrieval. Unfortunately, GANs do not offer an "inversemodel", a mapping from data space back to latent space, making it difficult toinfer a latent representation for a given data sample. In this paper, weintroduce a technique, inversion, to project data samples, specifically images,to the latent space using a pre-trained GAN. Using our proposed inversiontechnique, we are able to identify which attributes of a dataset a trained GANis able to model and quantify GAN performance, based on a reconstruction loss.We demonstrate how our proposed inversion technique may be used toquantitatively compare performance of various GAN models trained on three imagedatasets. We provide code for all of our experiments,https://github.com/ToniCreswell/InvertingGAN.
AU - Creswell,A
AU - Bharath,AA
PY - 2018///
TI - Inverting The Generator Of A Generative Adversarial Network (II)
UR - http://arxiv.org/abs/1802.05701v1
ER -