Awesome

WAE-WGAN

This repository contians an reimplmentation of WAE with Tensorflow.

I made one tweak on top of the paper. I used Wasserstein distance to penalize an encoder $Q_\phi$. In order to do that, I trained Discriminator $D_\gamma$ through critic loss with gradient penalty as Gularjani etal. suggested in [improved WGAN].

I (personally) believe that this implementation is much clearer and easy to read (, and more importantly, the code almost exactly matches with the algorithm shows on the paper), so I hope it will help someone who wants to digin more! Enjoy :beer:!

(Updates: 2018-May-1)

• MMD is implemented
  • Question 1: What is inverse multiscale kernel? The formula looks a little bit different from other resources..
  • Qeustion 2: On its original implementation, why MMD is evaluated on multiple scale (differnt C values) even though true scale is given as a prior? Doesn't it result in wrong MMD values and make Q_z diverge from P_z?

Requirements

• Python 3.x (tested with Python 3.5)
• TF v1.x (tested with 1.7rc0)
• tqdm
• and etc... (please report if you find other deps.)

Run

Training

python main.py

Check main.py file to change target dataset or to adjust hyperparmeters such as z_dim, and etc...

Inference

See the MNIST Plot.ipynb and CelebA Plot.ipynb with Jupyter Notebook.

A pretrained model for both MNIST is included on the repository while a model for CelebA is uploaded on this place. Please download the zip file and decompress it on assets/pretrained_models/celeba/last*. Or, you can easily modify a path at the first cell on the notebook.

Results

MNIST

• Trained with GTX-1080 Ti GPU for about 30 minutes

• Learning statistics

• Reconstruction Results (top): original images from MNIST validation set, (bottom): reconstructed image

  It seems not sharp as the authors suggest, but it might due to not enough training and untuned hyperparameters such as lambda, number of layers, or etc.

• Random Sampled Images

CelebA

• Trained with GTX-1080 Ti GPU for about 1 day.
• Encode and Decode images size of 64 by 64.
• Reconstruction Results (top): original images from CelebA validation set, (bottom): reconstructed image
• Random Sampled Images With fully trained model, the results seem pretty nice! Can we still say that AE-variants generating blurry images?
• Intepolation on $z$ space.

TODO

• Other datasets (CelebA, or CIFAR10)
• WAE-MMD
• WAE-GAN

Acknowledgement

• The author's original TF implementation link