Corpus ID: 220768931

Interpreting Spatially Infinite Generative Models

@article{Lu2020InterpretingSI,
  title={Interpreting Spatially Infinite Generative Models},
  author={Chaochao Lu and R. Turner and Yingzhen Li and Nate Kushman},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.12411}
}

Chaochao Lu, R. Turner, +1 author Nate Kushman

Published 2020

Computer Science, Mathematics

ArXiv

Traditional deep generative models of images and other spatial modalities can only generate fixed sized outputs. The generated images have exactly the same resolution as the training images, which is dictated by the number of layers in the underlying neural network. Recent work has shown, however, that feeding spatial noise vectors into a fully convolutional neural network enables both generation of arbitrary resolution output images as well as training on arbitrary resolution training images… CONTINUE READING

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