Tensorlayer implementation of InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.

Changing will rotate the digits:

Changing will change the width of the digits:

Changing will change the type of digits:

G_loss increases steadily after a sufficient number of iterations, showing the discriminator is getting stronger and stronger and indicating the end of training.

Azimuth (pose):

Presence or absence of glasses:

Hair color:

Hair quantity:

Lighting:

• Start training using python train.py; this will automatically download the dataset.
• To see the results, execute python test.py and input the number of your saved model.

• Set your image folder in config.py.
• Some links for the datasets:
  • CelebA
• Start training.

python train.py

• Set your data folder in config.py.
• A link for BFM 2009:
  • Basel Face Model. This should be downloaded before generating data.
  • Data is generated using the code in data_generator. Call gen_data in MATLAB.
• Start training using python train.py.
• To see the results, execute python test.py and input the number of your saved model.

• Set your image folder in data.py.
• Some links for the datasets:
  • Chairs
• Start training.

python train.py

1. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
2. Large-scale CelebFaces Attributes (CelebA) Dataset
3. THE MNIST DATABASE of handwritten digits
4. Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models

• 李舒辰 (@lisc55): The experiment on CelebA.
• 王畅 (@wangchang327): The experiment on MNIST.
• 竺沈涵 (@zshCuanNi): The experiment on Faces. Finished by 王畅 (@wangchang327).
• 岳鹏云 (@hswd40): The experiment on Chairs.