Collection of Generative Models with PyTorch
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine.
Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.
GAN/{gan_model}/out
VAE/{vae_model}/out
Vanilla GAN
Conditional GAN
InfoGAN
Wasserstein GAN
Mode Regularized GAN
Coupled GAN
Auxiliary Classifier GAN
Least Squares GAN
Boundary Seeking GAN
Energy Based GAN
f-GAN
Generative Adversarial Parallelization
DiscoGAN
Adversarial Feature Learning & Adversarially Learned Inference
Boundary Equilibrium GAN
Improved Training for Wasserstein GAN
DualGAN
MAGAN: Margin Adaptation for GAN
Softmax GAN
GibbsNet
Vanilla VAE
Conditional VAE
Denoising VAE
Adversarial Autoencoder
Adversarial Variational Bayes
Binary RBM with Contrastive Divergence
Binary RBM with Persistent Contrastive Divergence
Binary Helmholtz Machine with Wake-Sleep Algorithm
Install miniconda http://conda.pydata.org/miniconda.html
Do conda env create
conda env create
Enter the env source activate generative-models
source activate generative-models
Install Tensorflow
Install Pytorch
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