This is an implementation of Improved Training of Wasserstein GANs in Chainer v3.0.0.

Requirements

Chainer v3.0.0, OpenCV, etc.
The scripts work on Python 2.7.13 and 3.6.1.

How to generate images

$ python generate_image.py example_food-101/config.py -p example_food-101/trained-params_gen_update-000040000.npz

You can generate fixed images by specifying the random_seed option.

$ python generate_image.py example_food-101/config.py -r 1 -p example_food-101/trained-params_gen_update-000040000.npz

Example Food-101

Example Birds

Dataset

I resized the images to 64x64 before training.

• Food-101
  Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc. Food-101 -- Mining Discriminative Components with Random Forests. European Conference on Computer Vision, 2014.
• Birds
  Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. A Maximum Entropy Framework for Part-Based Texture and Object Recognition. Proceedings of the IEEE International Conference on Computer Vision, Beijing, China, October 2005, vol. 1, pp. 832-838.