https://github.com/wiseodd/generative-models

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
https://github.com/wiseodd/generative-models

gan generative-model machine-learning pytorch rbm restricted-boltzmann-machine tensorflow vae

Last synced: 3 days ago
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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

• Host: GitHub
• URL: https://github.com/wiseodd/generative-models
• Owner: wiseodd
• License: unlicense
• Created: 2016-12-07T11:28:16.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2024-03-24T20:38:46.000Z (9 months ago)
• Last Synced: 2024-12-11T16:06:16.574Z (10 days ago)
• Topics: gan, generative-model, machine-learning, pytorch, rbm, restricted-boltzmann-machine, tensorflow, vae
• Language: Python
• Homepage: http://wiseodd.github.io
• Size: 111 KB
• Stars: 7,349
• Watchers: 297
• Forks: 2,039
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://arxiv.org/abs/1406.2661)

  2. [Conditional GAN](https://arxiv.org/abs/1411.1784)

  3. [InfoGAN](https://arxiv.org/abs/1606.03657)

  4. [Wasserstein GAN](https://arxiv.org/abs/1701.07875)

  5. [Mode Regularized GAN](https://arxiv.org/abs/1612.02136)

  6. [Coupled GAN](https://arxiv.org/abs/1606.07536)

  7. [Auxiliary Classifier GAN](https://arxiv.org/abs/1610.09585)

  8. [Least Squares GAN](https://arxiv.org/abs/1611.04076v2)

  9. [Boundary Seeking GAN](https://arxiv.org/abs/1702.08431)

  10. [Energy Based GAN](https://arxiv.org/abs/1609.03126)

  11. [f-GAN](https://arxiv.org/abs/1606.00709)

  12. [Generative Adversarial Parallelization](https://arxiv.org/abs/1612.04021)

  13. [DiscoGAN](https://arxiv.org/abs/1703.05192)

  14. [Adversarial Feature Learning](https://arxiv.org/abs/1605.09782) & [Adversarially Learned Inference](https://arxiv.org/abs/1606.00704)

  15. [Boundary Equilibrium GAN](https://arxiv.org/abs/1703.10717)

  16. [Improved Training for Wasserstein GAN](https://arxiv.org/abs/1704.00028)

  17. [DualGAN](https://arxiv.org/abs/1704.02510)

  18. [MAGAN: Margin Adaptation for GAN](https://arxiv.org/abs/1704.03817)

  19. [Softmax GAN](https://arxiv.org/abs/1704.06191)

  20. [GibbsNet](https://papers.nips.cc/paper/7094-gibbsnet-iterative-adversarial-inference-for-deep-graphical-models.pdf)

#### Variational Autoencoder (VAE)

  1. [Vanilla VAE](https://arxiv.org/abs/1312.6114)

  2. [Conditional VAE](https://arxiv.org/abs/1406.5298)

  3. [Denoising VAE](https://arxiv.org/abs/1511.06406)

  4. [Adversarial Autoencoder](https://arxiv.org/abs/1511.05644)

  5. [Adversarial Variational Bayes](https://arxiv.org/abs/1701.04722)

#### Restricted Boltzmann Machine (RBM)

  1. [Binary RBM with Contrastive Divergence](http://www.cs.toronto.edu/~fritz/absps/cdmiguel.pdf)

  2. [Binary RBM with Persistent Contrastive Divergence](http://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf)

#### Helmholtz Machine

  1. [Binary Helmholtz Machine with Wake-Sleep Algorithm](http://www.cs.toronto.edu/~fritz/absps/ws.pdf)

## Dependencies

1. Install miniconda 

2. Do `conda env create`

3. Enter the env `source activate generative-models`

4. Install [Tensorflow](https://www.tensorflow.org/get_started/os_setup)

5. Install [Pytorch](https://github.com/pytorch/pytorch#installation)