VAE/GAN

the tensorflow code of Autoencoding beyond pixels using a learned similarity metric

The paper should be the first one to combine the Variational Autoencoder(VAE) and Generative Adversarial Networks(GAN), by using the discrimiator of GAN as the perceptual loss instead of the pixel-wise loss in the original VAE. VAE/GAN also can be used for image reconstruction and visual attribution manipulation.

About training instability

I also found the training is very instability. So, I update the code to stablize the adversarial progress of VAE/GAN. The details is in the below.

• Add a trick, named label smoothing Improved Techniques for Training GANs

Pretrained models.

The checkpoints files can be downloads from Google Drive. Please unzip the files inside the project directory. Later, I will update the new models after more training iterations.

Prerequisites

• tensorflow >=1.4

dataset requirement

You can download the Align and Cropped CelebA dataset and unzip CelebA into a directory. Noted that this directory don't contain the sub-directory.

Usage

Train:

$ python main.py --op 0 --path your data path

Test:

$ python main.py --op 1 --path your data path

Experiments visual result

Input:

Reconstruction

Issue

If you find the bug and problem, Thanks for your issue to propose it.

Reference code

DCGAN

autoencoding_beyond_pixels