Generative Models

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine.

Note:

Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

What’s in it?

Generative Adversarial Nets (GAN)

1. Vanilla GAN
2. Conditional GAN
3. InfoGAN
4. Wasserstein GAN
5. Mode Regularized GAN
6. Coupled GAN
7. Auxiliary Classifier GAN
8. Least Squares GAN
9. Boundary Seeking GAN
10. Energy Based GAN
11. f-GAN
12. Generative Adversarial Parallelization
13. DiscoGAN
14. Adversarial Feature Learning & Adversarially Learned Inference
15. Boundary Equilibrium GAN
16. Improved Training for Wasserstein GAN
17. DualGAN
18. MAGAN: Margin Adaptation for GAN
19. Softmax GAN
20. GibbsNet

Variational Autoencoder (VAE)

1. Vanilla VAE
2. Conditional VAE
3. Denoising VAE
4. Adversarial Autoencoder
5. Adversarial Variational Bayes

Restricted Boltzmann Machine (RBM)

1. Binary RBM with Contrastive Divergence
2. Binary RBM with Persistent Contrastive Divergence

Helmholtz Machine

1. Binary Helmholtz Machine with Wake-Sleep Algorithm

Dependencies

1. Install miniconda http://conda.pydata.org/miniconda.html
2. Do conda env create
3. Enter the env source activate generative-models
4. Install Tensorflow
5. Install Pytorch