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Loss-Sensitive GAN

Author: Guo-Jun Qi, Date: 1/9/2017, most recent update: 2/8/2017

Questions about the source codes can be directed to Dr. Guo-Jun Qi at [email protected]. All copyrights reserved.

Please cite the following paper when referring to the following algorithms (LS-GAN and CLS-GAN)

Guo-Jn Qi. Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities. arXiv:1701.06264 [pdf]

We are keeping updating this repository of source codes, and more results and algorithms will be released soon.

For LS-GAN (without conditions)

  1. Please download bedroom_train_lmdb from http://lsun.cs.princeton.edu

  2. Prepare the dataset following the instructions below

  3. Install LMDB in your system: sudo apt-get install liblmdb-dev

  4. Install torch packages: luarocks install lmdb.torch luarocks install tds

  5. Once downloading bedroom_train_lmdb, unzip the dataset and put it in a directory lsun/train

  6. Create an index file : Copy lsun_index_generator.lua to lsun/train, and run cd lsun/train DATA_ROOT=. th lsun_index_generator.lua Now you should have bedroom_train_lmdb_hashes_chartensor.t7 in lsun/train

  7. Now return to the parent direcotry of lsun, and you should be ready to run lsgan.lua: DATA_ROOT=lsun th lsgan.lua

How to display the generated images

To display images during training and generation, we will use the display package.

  • Install it with: luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
  • Then start the server with: th -ldisplay.start
  • Open this URL in your browser: http://localhost:8000

For Conditional LS-GAN (CLS-GAN)

  1. Download and prepare datasets

  2. MNIST: run torch-rocks install https://raw.github.com/andresy/mnist/master/rocks/mnist-scm-1.rockspec

  3. CIFAR10: run th ./data/Cifar10bintoTensor.lua

  4. SVHN: run th ./data/svhn.lua

  5. Now you should be able to run clsgan.lua now. Select the dataset you want to use. For example you want to run MNIST to generate handwritten digits according to ten digit classes. Then you can run the following command line

    dataset=mnist th clsgan.lua

    For the other parameters you can set, please refer to the script in clsgan.lua.

Acknowledge:

  1. parts of codes are reused from DCGAN at https://github.com/Newmu/dcgan_code

  2. the code downloading cifar10 is available at https://github.com/soumith/cifar.torch

  3. the code downloading SVHN: http://ufldl.stanford.edu/housenumbers/