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