Awesome-GANs with Tensorflow

Tensorflow implementation of GANs(Generative Adversarial Networks)

Environments

Local Environment

• OS : Windows 10 Edu x86-64 / Linux Ubuntu 16.04 x86-64
• CPU : i7-7700K
• GPU : GTX 1060 6GB
• RAM : DDR4 16GB
• Library : TF 1.5 with CUDA 9.0 + cuDNN 7.0
• Python 3.x

Preferred Environment

• OS : Linux Ubuntu 14.04 x86-64 ~
• CPU : any (quad core ~)
• GPU : GTX 1060 6GB ~
• RAM : DDR4 16GB ~
• Library : TF 1.4~ 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' :).

Prerequisites

• python 3.5+
• tensorflow 1.4.0
• scipy
• pillow
• h5py
• tqdm
• sklearn
• Internet :)

Usage

(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

DataSets

Now supporting(?) DataSets are... (code is in /datasets.py)

• MNIST
• CiFar-10
• CiFar-100
• Celeb-A
• pix2pix DataSets
• ImageNet
• (more DataSets will be added soon!)

Repo Tree

│
├── xxGAN
│    ├──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)
│    ├── gan_tb.png   (Tensor-Board result)
│    └── readme.md    (results & explains)
├── image_utils.py    (image processing)
└── datasets.py       (DataSet loader)

Papers & Codes

Name	Summary	Paper	Code
ACGAN	Auxiliary Classifier Generative Adversarial Networks	[arXiv]	[code]
AdaGAN	Boosting Generative Models	[arXiv]
AnoGAN	Unsupervised Anomaly Detection with Generative Adversarial Networks	[arXiv]
BEGAN	Boundary Equilibrium Generative Adversarial Networks	[arXiv]	[code]
BGAN	Boundary-Seeking Generative Adversarial Networks	[arXiv]	[code]
CGAN	Conditional Generative Adversarial Networks	[arXiv]	[code]
CipherGAN	Unsupervised Cipher Cracking Using Discrete GANs	[arXiv]
CoGAN	Coupled Generative Adversarial Networks	[arXiv]
CycleGAN	Unpaired img2img translation using Cycle-consistent Adversarial Networks	[arXiv]	[code]
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]
eCommerceGAN	A Generative Adversarial Network for E-commerce	[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]

Author

HyeongChan Kim / (@kozistr, @zer0day)