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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
This is free and unencumbered software released into the public domain. Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means. In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit of the public at large and to the detriment of our heirs and successors. We intend this dedication to be an overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. For more information, please refer to <http://unlicense.org>

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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. 展开 收起
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