boundary-seeking generative adversarial networks
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Boundary-seeking generative adversarial networks (BGAN)

as featured in the paper:

Requirements (rough estimate)

Basic instructions

Note: Very basic. In-depth instuctions forthcoming.

Datasets are available via Fuel:

Install MNIST:

$ cd <Dataset directory>

$ fuel-download binarized_mnist

$ fuel-convert binarized_mnist

Install CelebA:

$ cd <Dataset directory>

$ fuel-download celeba

$ fuel-convert celeba 64


For simple BGAN running on discrete MNIST:

python -o <Output directory -S <Path to MNIST hdf5>

For simple BGAN running on continuous CelebA:

python -o <Output directory> -S <Path to CelebA hdf5>

Basic documentation found in:

python --help

Note: Published versions of the model are available in the code, and instructions to reproduce will be added soon.

If there are bugs or clarity is needed to run models, please add to the Issues.