Invertible Conditional GANs for image editing
Abstract
Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.
Cite
@article{arxiv.1611.06355,
title = {Invertible Conditional GANs for image editing},
author = {Guim Perarnau and Joost van de Weijer and Bogdan Raducanu and Jose M. Álvarez},
journal= {arXiv preprint arXiv:1611.06355},
year = {2016}
}
Comments
Accepted paper at NIPS 2016 Workshop on Adversarial Training