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Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

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InfoGAN

InfoGAN Architecture

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

Results

MNIST

Manipulating the First Continuous Latent Code

Changing will rotate the digits:

Manipulating the Second Continuous Latent Code

Changing will change the width of the digits:

Manipulating the Discrete Latent Code (Categorical)

Changing will change the type of digits:

Random Generation and Loss Plot

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

CelebA

Manipulating Discrete Latent Code

Azimuth (pose):

Presence or absence of glasses:

Hair color:

Hair quantity:

Lighting:

Faces

Loss Plot

Azimuth

Random Generation

Chairs

Rotation

Run

MNIST

  • 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.

CelebA

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

Faces

  • 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.

Chairs

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

References

  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

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