%0 Journal Article %1 im2016generating %A Im, Daniel Jiwoong %A Kim, Chris Dongjoo %A Jiang, Hui %A Memisevic, Roland %D 2016 %K deep-learning generative-models %T Generating images with recurrent adversarial networks %U http://arxiv.org/abs/1602.05110 %X 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.

@article{im2016generating, 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.}, added-at = {2019-04-23T13:13:33.000+0200}, author = {Im, Daniel Jiwoong and Kim, Chris Dongjoo and Jiang, Hui and Memisevic, Roland}, biburl = {https://www.bibsonomy.org/bibtex/275982e9b7f1aaa21e00f8ce9c07ba101/kirk86}, description = {[1602.05110] Generating images with recurrent adversarial networks}, interhash = {e43416aaa4961045a0ea27025ad80728}, intrahash = {75982e9b7f1aaa21e00f8ce9c07ba101}, keywords = {deep-learning generative-models}, note = {cite arxiv:1602.05110}, timestamp = {2019-04-23T13:13:33.000+0200}, title = {Generating images with recurrent adversarial networks}, url = {http://arxiv.org/abs/1602.05110}, year = 2016 }