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Collection of Generative Models with PyTorch

2019-09-11 | |  178 |   0 |   0

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

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