pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks." arXiv preprint arXiv:1706.04987 (2017).

work in progress. paper results not yet reproduced

In the original paper, prior and posterior terms appear to be swapped in the code discriminator loss (equations 16 and 17 in Algorithm 1).

Algorithm 1 in the paper is generally vague as to how each network should be updated. In this implementation:

• Encoder and generator are trained jointly
• Discriminator and code discriminator are trained jointly
• As in other GAN implementations, discriminator is updated first, then generator for each batch.

alphagan/examples/CIFAR.ipynb

from alphagan import AlphaGAN

encoder, generator, D, C = ... #torch.nn.Module

model = AlphaGAN(encoder, generator, D, C, lambd=10, latent_dim=32)

X_train, X_valid = ... #torch.utils.data.DataSet

train_loader, valid_loader = ... #torch.utils.data.DataLoader

model.fit(train_loader, valid_loader, n_epochs=80, log_fn=print)

# encode and reconstruct
z_valid, x_recon = model(X_valid[0])

# sample from the generative model
z, x_gen = model(batch_size, mode='sample')

Supply any torch.nn.Module decoder, generator, discriminator, and code discriminator at construction and any torch.optim.Optimizer and torch.utils.DataLoader to fit().

Install tqdm for progress bars. To get working nested progress bars in jupyter notebooks: pip install -e git+https://github.com/dvm-shlee/tqdm.git@master#egg=tqdm