From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators

@article{Upchurch2016FromAT,
  title={From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators},
  author={Paul Upchurch and Noah Snavely and Kavita Bala},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.02003}
}
We propose a new neural network architecture for solving single-image analogies - the generation of an entire set of stylistically similar images from just a single input image. Solving this problem requires separating image style from content. Our network is a modified variational autoencoder (VAE) that supports supervised training of single-image analogies and in-network evaluation of outputs with a structured similarity objective that captures pixel covariances. On the challenging task of… CONTINUE READING
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