Tensorflow implementation of GANs(Generative Adversarial Networks)
- OS : Windows 10 Edu x86-64 / Linux Ubuntu 16.04 x86-64
- CPU : i7-7700K
- GPU : GTX 1060 6GB
- RAM : DDR4 16GB
- Library : TF 1.4.1 with CUDA 8.0 + cuDNN 7.0
- Python 3.x
- OS : Any
- CPU : Any
- GPU : None or Any ('compute capability of GPU' must be over 3.0)
- RAM : 2GB ~
- Library : TF 1.4.0 ~ with CUDA 8.0 + cuDNN 7.0 ~
- Python 3.x
- OS : Linux Ubuntu 14.04 x86-64 ~
- CPU : any (quad core ~)
- GPU : GTX 1060 6GB ~
- RAM : DDR4 16GB ~
- Library : TF 1.4.0 ~ with CUDA 8.0 + cuDNN 7.0 ~
- Python 3.x
Because of the image and model size, (especially BEGAN, SGAN, SRGAN, StarGAN, ... using high resolution images as input), if you want to train them comfortably, you need a GPU which has more than 8GB.
But, of course, the most of the implementations use MNIST or CiFar-10, 100 DataSets. Meaning that we can handle it with EVEN lower spec GPU than 'The Preferred' :).
- python 3.5+
- tensorflow-gpu or tensorflow 1.4.0
- scipy
- pillow
- h5py
- pickle
- tqdm
- sklearn
- Internet :)
(before running train.py, make sure run after downloading dataset & changing dataset directory in train.py)
just download it and run train.py
$ python3 xxx_train.py
Now supporting(?) DataSets are... (code is in /datasets.py)
- MNIST
- CiFar-10
- CiFar-100
- Celeb-A
pix2pix shoespix2pix bagsImageNet- (more DataSets will be added soon!)
│
├── gan_img (generated images)
│ ├── train_xxx.png
│ └── train_xxx.png
├── model (model)
│ ├── checkpoint
│ ├── ...
│ └── xxx.ckpt
├── gan_model.py (gan model)
├── gan_train.py (gan trainer)
├── dataset.py (DataSet loader)
├── gan_tb.png (Tensor-Board result)
└── readme.md (results & explains)
| Name | Summary | Paper | Code |
|---|---|---|---|
| ACGAN | Auxiliary Classifier Generative Adversarial Networks | [arXiv] | [code] |
| AdaGAN | Boosting Generative Models | [arXiv] | |
| BEGAN | Boundary Equilibrium Generative Adversarial Networks | [arXiv] | [code] |
| BGAN | Boundary-Seeking Generative Adversarial Networks | [arXiv] | [code] |
| CGAN | Conditional Generative Adversarial Networks | [arXiv] | [code] |
| CoGAN | Coupled Generative Adversarial Networks | [arXiv] | |
| CycleGAN | Unpaired img2img translation using Cycle-consistent Adversarial Networks | [arXiv] | |
| DCGAN | Deep Convolutional Generative Adversarial Networks | [arXiv] | [code] |
| DiscoGAN | Discover Cross-Domain Generative Adversarial Networks | [arXiv] | |
| DualGAN | Unsupervised Dual Learning for Image-to-Image Translation | [arXiv] | |
| EBGAN | Energy-based Generative Adversarial Networks | [arXiv] | [code] |
| f-GAN | Training Generative Neural Samplers using Variational Divergence Minimization | [arXiv] | |
| GAN | Generative Adversarial Networks | [arXiv] | [code] |
| Softmax GAN | Generative Adversarial Networks with Softmax | [arXiv] | [code] |
| 3D GAN | 3D Generative Adversarial Networks | [MIT] | |
| GAP | Generative Adversarial Parallelization | [arXiv] | |
| GEGAN | Generalization and Equilibrium in Generative Adversarial Networks | [arXiv] | |
| InfoGAN | Interpretable Representation Learning by Information Maximizing Generative Adversarial Networks | [arXiv] | [code] |
| LAPGAN | Laplacian Pyramid Generative Adversarial Networks | [arXiv] | [code] |
| LSGAN | Loss-Sensitive Generative Adversarial Networks | [arXiv] | [code] |
| MAGAN | Margin Adaptation for Generative Adversarial Networks | [arXiv] | [code] |
| MRGAN | Mode Regularized Generative Adversarial Networks | [arXiv] | |
| SalGAN | Visual Saliency Prediction Generative Adversarial Networks | [arXiv] | |
| SeqGAN | Sequence Generative Adversarial Networks with Policy Gradient | [arXiv] | |
| SGAN | Stacked Generative Adversarial Networks | [arXiv] | [code] |
| SGAN++ | Realistic Image Synthesis with Stacked Generative Adversarial Networks | [arXiv] | |
| SRGAN | Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | [arXiv] | [code] |
| StarGAN | Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation | [arXiv] | [code] |
| WGAN | Wasserstein Generative Adversarial Networks | [arXiv] | [code] |
| ImprovedWGAN | Improved Training of Wasserstein Generative Adversarial Networks | [arXiv] | [code] |