Generative Models

December 22, 2017 ยท View on GitHub

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