Generating images with recurrent adversarial networks
Daniel Jiwoong Im
and
Chris Dongjoo Kim
and
Hui Jiang
and
Roland Memisevic
arXiv e-Print archive - 2016 via Local arXiv
Keywords:
cs.LG, cs.CV
First published: 2016/02/16 (8 years ago) Abstract: Gatys et al. (2015) showed that optimizing pixels to match features in a
convolutional network with respect reference image features is a way to render
images of high visual quality. We show that unrolling this gradient-based
optimization yields a recurrent computation that creates images by
incrementally adding onto a visual "canvas". We propose a recurrent generative
model inspired by this view, and show that it can be trained using adversarial
training to generate very good image samples. We also propose a way to
quantitatively compare adversarial networks by having the generators and
discriminators of these networks compete against each other.
