“Generating Images With Recurrent Adversarial Networks”, 2016-02-16 (; similar):
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.