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Coupled Generative Adversarial Network code

General

This is an implementation of the Coupled Generative Adversarial Netowork algorithm. For more details please refer to our NIPS paper.

Please cite CoGAN in your publications if it helps your research:

@inproceedings{liu2016coupled, title={Coupled Generative Adversarial Networks}, author={Liu, Ming-Yu and Tuzel, Oncel}, booktitle={NIPS}, year={2016} }

Content

caffe/

The folder contains a modified version of Caffe. It includes a new solver and several new layers for supporting back propagation with alternating gradient update steps. Note that several existing classes were modified for the same need.

cogan/

The folder contains the solver and network definition files.

build.sh

A script for building the Caffe library.

train_cogan.sh

A script for train a CoGAN net.

test_cogan.sh

A script for visualizing CoGAN training results.

Usage

./build.sh ./train_cogan.sh ./test_cogan.sh

Copyright 2016, Ming-Yu Liu

All Rights Reserved

Permission to use, copy, modify, and distribute this software and its documentation for any non-commercial purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of the author not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission.

THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

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  • Jupyter Notebook 57.4%
  • C++ 32.9%
  • Python 4.3%
  • Cuda 2.3%
  • CMake 1.2%
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  • Other 1.2%