Generating images with recurrent adversarial networks

@article{Im2016GeneratingIW,
  title={Generating images with recurrent adversarial networks},
  author={Daniel Jiwoong Im and Chris Dongjoo Kim and Hui Jiang and Roland Memisevic},
  journal={ArXiv},
  year={2016},
  volume={abs/1602.05110}
}
Gatys et al. (2015) 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… CONTINUE READING

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