PT Unknown
AU Guim Perarnau
   Joost Van de Weijer
   Bogdan Raducanu
   Jose Manuel Alvarez
TI Invertible conditional gans for image editing
BT 30th Annual Conference on Neural Information Processing Systems Worshops
PY 2016
AB 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 encoderwith a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate realimages with deterministic complex modifications.
ER