GAN

Generative Adversarial Networks

Reproduce the following GAN-related methods:

• DCGAN (Unsupervised Representation Learning with DCGAN)

• pix2pix (Image-to-image Translation with Conditional Adversarial Networks)

• InfoGAN (InfoGAN: Interpretable Representation Learning by Information Maximizing GAN)

• Conditional GAN

• Wasserstein GAN

Please see the docstring in each script for detailed usage and pretrained models.

DCGAN-CelebA.py

Reproduce DCGAN following the setup in dcgan.torch.

• Generated samples

• Vector arithmetic: smiling woman - neutral woman + neutral man = smiling man

Image2Image.py

Image-to-Image following the setup in pix2pix.

For example, with the cityscapes dataset, it learns to generate semantic segmentation map of urban scene:

This is a visualization from tensorboard. Left to right: original, ground truth, model output.

InfoGAN-mnist.py

Reproduce the mnist experiement in InfoGAN. It assumes 10 latent variables corresponding to a categorical distribution, 2 latent variables corresponding to a uniform distribution. It then maximizes mutual information between these latent variables and the image, and learns interpretable latent representation.

• Left: 10 latent variables corresponding to 10 digits.
• Middle: 1 continuous latent variable controlled the rotation.
• Right: another continuous latent variable controlled the thickness.

ConditionalGAN-mnist.py

Train a simple GAN on mnist, conditioned on the class labels.

WGAN-CelebA.py

Reproduce Wasserstein GAN by some small modifications on DCGAN-CelebA.py.