GAN生成式对抗攻击代码

# 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 <http://conda.pydata.org/miniconda.html> 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)