showed that optimizing pixels to match features in a convolutional network is a way to render images of high visual quality. Unrolling this gradient-based optimization can be thought of as a recurrent computation, that creates images by incrementally adding onto a visual "canvas". Inspired by this view we propose a recurrent generative model that can be trained using adversarial training. In order to quantitatively compare adversarial networks we also propose a new performance measure, that is based on letting the generator and discriminator of two models compete against each other.
Im et al. REF propose a recurrent generative model trained using adversarial training.
Generating images with recurrent adversarial networks
ArXiv
ArXiv
Mathematics Computer Science