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https://github.com/kozistr/Awesome-GANs
Awesome Generative Adversarial Networks with tensorflow
https://github.com/kozistr/Awesome-GANs
List: Awesome-GANs
acgan arxiv began cgan cogan dcgan dragan ebgan f-gan gan generative-adversarial-network lapgan lsgan machine-learning sagan srgan stargan tensorflow wgan wgan-gp
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Awesome Generative Adversarial Networks with tensorflow
- Host: GitHub
- URL: https://github.com/kozistr/Awesome-GANs
- Owner: kozistr
- License: mit
- Created: 2017-05-28T14:00:08.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-25T12:03:16.000Z (over 2 years ago)
- Last Synced: 2024-05-23T04:06:07.675Z (5 months ago)
- Topics: acgan, arxiv, began, cgan, cogan, dcgan, dragan, ebgan, f-gan, gan, generative-adversarial-network, lapgan, lsgan, machine-learning, sagan, srgan, stargan, tensorflow, wgan, wgan-gp
- Language: Python
- Homepage:
- Size: 81.6 MB
- Stars: 760
- Watchers: 28
- Forks: 163
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-GAN-papers - 11
- awesome-ai-list-guide - Awesome-GANs
- awesome-machine-learning-resources - **[List - GANs?style=social) (Table of Contents)
- ultimate-awesome - Awesome-GANs - Awesome Generative Adversarial Networks with tensorflow. (Other Lists / PowerShell Lists)
README
# Awesome-GANs with Tensorflow
Tensorflow implementation of GANs (**Generative Adversarial Networks**)
[](https://github.com/sindresorhus/awesome)
[](https://opensource.org/licenses/MIT)
[](https://lgtm.com/projects/g/kozistr/Awesome-GANs/context:python)
## **WIP** : This repo is about to be refactored & supporting `tf 2.x`.
Maybe some codes wouldn't work on master branch, because i'm just working on the branch.
## Environments
Because of the image and model size, (especially **BEGAN**, **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' :).
## Usage
Now on **refactoring**... All GAN training script can be run module-wisely like below. (**WIP**)
### Install dependencies
You can also use *conda*, *virtualenv* environments.
```shell script
$ python3 -m pip install -r requirements.txt
```
### Train GANs
Before running the model, make sure that
1. downloading the dataset like *CelebA*, *MNIST*, etc what you want
2. In `awesome_gans/config.py`, there are several configurations, customize with your flavor!
3. running the model like below
```shell script
$ python3 -m awesome_gans.acgan
```
## DataSets
Supporting datasets are ... (code is in `/awesome_gans/datasets.py`)
* MNIST / ~~Fashion MNIST~~
* CIFAR10 / 100
* CelebA/CelebA-HQ
* Pix2Pix
* DIV2K
* (more DataSets will be added soon!)
## Repo Tree
```
│
├── awesome_gans (source codes & eplainations & results & models)
│ │
│ ├── acgan
│ │ ├──gen_img (generated images)
│ │ │ ├── train_xxx.png
│ │ │ └── train_xxx.png
│ │ ├── model (pre-trained model file)
│ │ │ └── model.txt (google-drive link)
│ │ ├── __init__.py
│ │ ├── __main__.py
│ │ ├── model.py (gan model)
│ │ ├── train.py (gan trainer)
│ │ ├── gan_tb.png (tensorboard loss plot)
│ │ └── readme.md (results & explainations)
│ ├── config.py (configurations)
│ ├── modules.py (networks & operations)
│ ├── utils.py (auxiliary utils)
│ ├── image_utils.py (image processing)
│ └── datasets.py (dataset loader)
├── CONTRIBUTING.md
├── Makefile (for linting the codes)
├── LICENSE
├── README.md (Usage & GAN paper list-up)
└── requirements.txt
```
## Papers & Codes
Here's the list-up for tons of GAN papers. all papers are sorted by alphabetic order.
### Start
Here's the beginning of the **GAN**.
| *Name* | *Summary* | *Paper* | *Code* |
|:-------:|:---------------------------------:|:------------------------------------------:|:----------------------------:|
| **GAN** | *Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1406.2661) | [[code]](./awesome_gans/GAN) |
### Theory & Concept
Here for the theories & concepts of the GAN.
| *Name* | *Summary* | *Paper* | *Code* | *Official Code* |
|:-----------------:|:-------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------:|:--------------------------------------------------------------------:|
| **ACGAN** | *Auxiliary Classifier Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1610.09585) | [[code]](./awesome_gans/ACGAN) |
| **AdaGAN** | *Boosting Generative Models* | [[arXiv]](https://arxiv.org/abs/1701.02386) | [[~~code~~]]() |
| **bCR** | *Improved Consistency Regularization for GANs* | [[arXiv]](https://arxiv.org/abs/2002.04724) | [[~~code~~]]() |
| **BEGAN** | *Boundary Equilibrium Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1703.10717) | [[code]](./awesome_gans/BEGAN) |
| **BGAN** | *Boundary-Seeking Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1702.08431) | [[code]](./awesome_gans/BGAN) |
| **BigGAN** | *Large Scale GAN Training for High Fidelity Natural Image Synthesis* | [[arXiv]](https://arxiv.org/abs/1809.11096) | [[~~code~~]]() |
| **CGAN** | *Conditional Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1411.1784) | [[code]](./awesome_gans/CGAN) |
| **CoGAN** | *Coupled Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1606.07536) | [[code]](./awesome_gans/CoGAN) |
| **ConSinGAN** | *Improved Techniques for Training Single-Image GANs* | [[WACV21]](https://openaccess.thecvf.com/content/WACV2021/papers/Hinz_Improved_Techniques_for_Training_Single-Image_GANs_WACV_2021_paper.pdf) | [[~~code~~]]() | [[official]](https://github.com/tohinz/ConSinGAN) |
| **DCGAN** | *Deep Convolutional Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1511.06434) | [[code]](./awesome_gans/DCGAN) |
| **DRAGAN** | *On Convergence and Stability of Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1705.07215) | [[code]](./awesome_gans/DRAGAN) |
| **EBGAN** | *Energy-based Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1609.03126) | [[code]](./awesome_gans/EBGAN) |
| **EqGAN-SA** | *Improving GAN Equilibrium by Raising Spatial Awareness* | [[arXiv]](https://arxiv.org/pdf/2112.00718.pdf) | [[~~code~~]]() | [[official]](https://github.com/genforce/eqgan-sa) |
| **f-GAN** | *Training Generative Neural Samplers using Variational Divergence Minimization* | [[arXiv]](https://arxiv.org/abs/1606.00709) | [[code]](./awesome_gans/FGAN) |
| **GP-GAN** | *Towards Realistic High-Resolution Image Blending* | [[arXiv]](https://arxiv.org/abs/1703.07195) | [[~~code~~]]() |
| **Softmax GAN** | *Generative Adversarial Networks with Softmax* | [[arXiv]](https://arxiv.org/abs/1704.06191) | [[code]](./awesome_gans/GAN) |
| **GAP** | *Generative Adversarial Parallelization* | [[arXiv]](https://arxiv.org/abs/1612.04021) | [[~~code~~]]() |
| **GEGAN** | *Generalization and Equilibrium in Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1703.00573) | [[~~code~~]]() |
| **G-GAN** | *Geometric GAN* | [[arXiv]](https://arxiv.org/abs/1705.02894) | [[~~code~~]]() |
| **InfoGAN** | *Interpretable Representation Learning by Information Maximizing Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1606.03657) | [[code]](./awesome_gans/InfoGAN) |
| **LAPGAN** | *Laplacian Pyramid Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1506.05751) | [[code]](./awesome_gans/LAPGAN) |
| **LSGAN** | *Loss-Sensitive Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1701.06264) | [[code]](./awesome_gans/LSGAN) |
| **MAGAN** | *Margin Adaptation for Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1704.03817) | [[code]](./awesome_gans/MAGAN) |
| **MRGAN** | *Mode Regularized Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1612.02136) | [[code]](./awesome_gans/MRGAN) |
| **MSGGAN** | *Multi-Scale Gradients for Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1903.06048) | [[~~code~~]]() |
| **PGGAN** | *Progressive Growing of GANs for Improved Quality, Stability, and Variation* | [[arXiv]](https://arxiv.org/abs/1710.10196) | [[~~code~~]]() | [[official]](https://github.com/tkarras/progressive_growing_of_gans) |
| **RaGAN** | *The relativistic discriminator: a key element missing from standard GAN* | [[arXiv]](https://arxiv.org/pdf/1807.00734v3.pdf) | [[~~code~~]]() |
| **SeAtGAN** | *Self-Attention Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1805.08318) | [[code]](./awesome_gans/SAGAN) |
| **SphereGAN** | *Sphere Generative Adversarial Network Based on Geometric Moment Matching* | [[CVPR2019]](http://cau.ac.kr/~jskwon/paper/SphereGAN_CVPR2019.pdf) | [[~~code~~]]() |
| **SGAN** | *Stacked Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1612.04357) | [[~~code~~]](https://github.com/kozistr/Awesome-GANs/blob/master/SGAN) |
| **SGAN++** | *Realistic Image Synthesis with Stacked Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1710.10916) | [[~~code~~]](https://github.com/kozistr/Awesome-GANs/blob/master/SGAN) |
| **SinGAN** | *Learning a Generative Model from a Single Natural Image* | [[arXiv]](https://arxiv.org/abs/1905.01164) | [[~~code~~]]() | [[official]](https://github.com/tamarott/SinGAN) |
| **StableGAN** | *Stabilizing Adversarial Nets With Prediction Methods* | [[arXiv]](https://arxiv.org/abs/1705.07364) | [[~~code~~]]() |
| **StyleCLIP** | *Text-Driven Manipulation of StyleGAN Imagery* | [[arXiv]](https://arxiv.org/abs/2103.17249) | [[~~code~~]]() | [[official]](https://github.com/orpatashnik/StyleCLIP) |
| **StyleGAN** | *A Style-Based Generator Architecture for Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1812.04948) | [[~~code~~]]() | [[official]](https://github.com/NVlabs/stylegan) |
| **StyleGAN2** | *Analyzing and Improving the Image Quality of StyleGAN* | [[arXiv]](http://arxiv.org/abs/1912.04958) | [[~~code~~]]() | [[official]](https://github.com/NVlabs/stylegan2) |
| **StyleGAN2 ADA** | *StyleGAN2 with adaptive discriminator augmentation* | [[arXiv]](https://arxiv.org/abs/2006.06676) | [[~~code~~]]() | [[official]](https://github.com/NVlabs/stylegan2-ada) |
| **StyleGAN3** | *Alias-Free Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/2106.12423) | [[~~code~~]]() | [[official]](https://github.com/NVlabs/stylegan3) |
| **StyleGAN-XL** | *Scaling StyleGAN to Large Diverse Datasets* | [[arXiv]](https://arxiv.org/abs/2202.00273) | [[~~code~~]]() | [[official]](https://github.com/autonomousvision/stylegan_xl) |
| **TripleGAN** | *Triple Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1703.02291) | [[~~code~~]]() |
| **UGAN** | *Unrolled Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1611.02163) | [[~~code~~]]() |
| **U-Net GAN** | *A U-Net Based Discriminator for Generative Adversarial Networks* | [[CVPR20]](https://openaccess.thecvf.com/content_CVPR_2020/html/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.html) | [[~~code~~]]() | [[official]](https://github.com/boschresearch/unetgan) |
| **WGAN** | *Wasserstein Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1701.07875) | [[code]](./awesome_gans/WGAN) |
| **WGAN-GP** | *Improved Training of Wasserstein Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1704.00028) | [[code]](./awesome_gans/WGAN) |
### Applied Vision
Here for the GAN applications on Vision domain,
like image-to-image translation, image in-painting, single image super resolution , etc.
| *Name* | *Summary* | *Paper* | *Code* | *Official Code* |
|:---------------:|:---------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------:|:---------------------------------------------------------------:|
| **3D GAN** | *3D Generative Adversarial Networks* | [[MIT]](http://3dgan.csail.mit.edu/) | [[~~code~~]]() |
| **AnycostGAN** | *Anycost GANs for Interactive Image Synthesis and Editing* | [[arXiv]](https://arxiv.org/abs/2103.03243) | [[~~code~~]]() | [[official]](https://github.com/mit-han-lab/anycost-gan) |
| **CycleGAN** | *Unpaired img2img translation using Cycle-consistent Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1703.10593) | [[code]](./awesome_gans/CycleGAN) |
| **DAGAN** | *Instance-level Image Translation by Deep Attention Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1802.06454) | [[~~code~~]]() |
| **DeblurGAN** | *Blind Motion Deblurring Using Conditional Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1711.07064) | [[~~code~~]]() |
| **DualGAN** | *Unsupervised Dual Learning for Image-to-Image Translation* | [[arXiv]](https://arxiv.org/abs/1704.02510) | [[~~code~~]]() |
| **DRIT/++** | *Diverse Image-to-Image Translation via Disentangled Representations* | [[arXiv]](https://arxiv.org/abs/1905.01270) | [[~~code~~]]() | [[official]](https://github.com/HsinYingLee/DRIT) |
| **EdgeConnect** | *Generative Image Inpainting with Adversarial Edge Learning* | [[arXiv]](https://arxiv.org/abs/1901.00212) | [[~~code~~]]() | [[official]](https://github.com/knazeri/edge-connect) |
| **ESRGAN** | *Enhanced Super-Resolution Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1809.00219) | [[~~code~~]]() |
| **FastGAN** | *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis* | [[arXiv]](https://arxiv.org/abs/2101.04775) | [[~~code~~]]() | [[official]](https://github.com/odegeasslbc/FastGAN-pytorch) |
| **FUNIT** | *Few-Shot Unsupervised Image-to-Image Translation* | [[arXiv]](https://arxiv.org/abs/1905.01723) | [[~~code~~]]() | [[official]](https://github.com/NVlabs/FUNIT) |
| **CA & GA** | *Generative Image Inpainting w/ Contextual Attention & Gated Convolution* | [[CVPR2018]](https://arxiv.org/abs/1801.07892), [[ICCV2019]](https://arxiv.org/abs/1806.03589) | [[~~code~~]]() | [[official]](https://github.com/JiahuiYu/generative_inpainting) |
| **HiFaceGAN** | *Face Renovation via Collaborative Suppression and Replenishment* | [[arXiv]](https://arxiv.org/abs/2005.05005v1) | [[~~code~~]]() |
| **MUNIT** | *Multimodal Unsupervised Image-to-Image Translation* | [[arXiv]](https://arxiv.org/abs/1804.04732) | [[~~code~~]]() | [[official]](https://github.com/NVlabs/MUNIT) |
| **NICE-GAN** | *Reusing Discriminators for Encoding* | [[arXiv]](https://arxiv.org/abs/2003.00273) | [[~~code~~]]() | [[official]](https://github.com/alpc91/NICE-GAN-pytorch) |
| **PSGAN** | *Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer* | [[arXiv]](https://arxiv.org/abs/1909.06956) | [[~~code~~]]() | [[official]](https://github.com/wtjiang98/PSGAN) |
| **SpAtGAN** | *Generative Adversarial Network with Spatial Attention for Face Attribute Editing* | [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/html/Gang_Zhang_Generative_Adversarial_Network_ECCV_2018_paper.html) | [[~~code~~]]() |
| **SalGAN** | *Visual Saliency Prediction Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1701.01081) | [[~~code~~]]() |
| **SRFlow** | *Super-Resolution using Normalizing Flow* | [[arXiv]](https://arxiv.org/abs/2006.14200) | [[~~code~~]]() | [[official]](https://github.com/andreas128/SRFlow) |
| **SRGAN** | *Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network* | [[arXiv]](https://arxiv.org/abs/1609.04802) | [[code]](./awesome_gans/SRGAN) |
| **SRResCGAN** | *Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution* | [[arXiv]](https://arxiv.org/abs/2005.00953) | [[~~code~~]]() | [[official]](https://github.com/RaoUmer/SRResCGAN) |
| **StarGAN** | *Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation* | [[arXiv]](https://arxiv.org/abs/1711.09020) | [[code]](./awesome_gans/StarGAN) | [[official]](https://github.com/yunjey/stargan) |
| **StarGAN V2** | *Diverse Image Synthesis for Multiple Domains* | [[arXiv]](https://arxiv.org/abs/1912.01865) | [[~~code~~]]() | [[official]](https://github.com/clovaai/stargan-v2) |
| **StyleGAN-V** | *A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2* | [[arXiv]](https://kaust-cair.s3.amazonaws.com/stylegan-v/stylegan-v-paper.pdf) | [[~~code~~]]() | [[official]](https://github.com/universome/stylegan-v) |
| **TecoGAN** | *Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation* | [[arXiv]](https://arxiv.org/abs/1811.09393) | [[~~code~~]]() | [[official]](https://github.com/thunil/TecoGAN) |
| **TextureGAN** | *Controlling Deep Image Synthesis with Texture Patches* | [[arXiv]](https://arxiv.org/abs/1706.02823) | [[~~code~~]]() |
| **TUNIT** | *Rethinking the Truly Unsupervised Image-to-Image Translation* | [[arXiv]](https://arxiv.org/abs/2006.06500) | [[~~code~~]]() | [[official]](https://github.com/clovaai/tunit) |
| **TwinGAN** | *Cross-Domain Translation fo Human Portraits* | [[github]](https://github.com/jerryli27/TwinGAN) | [[~~code~~]]() |
| **UNIT** | *Unsupervised Image-to-Image Translation Networks* | [[arXiv]](https://arxiv.org/abs/1703.00848) | [[~~code~~]]() | [[official]](https://github.com/mingyuliutw/UNIT) |
| **XGAN** | *Unsupervised Image-to-Image Translation for Many-to-Many Mappings* | [[arXiv]](https://arxiv.org/abs/1711.05139) | [[~~code~~]]() |
| **Zero-DCE** | *Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement* | [[CVPR20]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf) | [[~~code~~]]() | [[official]](https://github.com/Li-Chongyi/Zero-DCE) |
### Applied Audio
Here for the GAN applications on Audio domain,
like wave generation, wave to wave translation, etc.
| *Name* | *Summary* | *Paper* | *Code* | *Official Code* |
|:--------------------:|:--------------------------------------------------------------------------------------:|:------------------------------------------------------------:|:--------------:| :---: |
| **AAS** | *Adversarial Audio Synthesis* | [[arXiv]](https://arxiv.org/abs/1802.04208) | [[~~code~~]]() | |
| **BeatGAN** | *Generating Drum Loops via GANs* | [[arXiv]](https://github.com/NarainKrishnamurthy/BeatGAN2.0) | [[~~code~~]]() | |
| **GANSynth** | *Adversarial Neural Audio Synthesis* | [[arXiv]](https://arxiv.org/abs/1902.08710) | [[~~code~~]]() | |
| **MuseGAN** | *Multi-track Sequential GANs for Symbolic Music Generation and Accompaniment* | [[arXiv]](https://arxiv.org/abs/1709.06298) | [[~~code~~]]() | |
| **SEGAN** | *Speech Enhancement Generative Adversarial Network* | [[arXiv]](https://arxiv.org/abs/1703.09452) | [[~~code~~]]() | |
| **StarGAN-VC** | *Non-parallel many-to-many voice conversion with star generative adversarial networks* | [[arXiv]](https://arxiv.org/abs/1806.02169) | [[~~code~~]]() | |
| **TempoGAN** | *A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow* | [[arXiv]](https://arxiv.org/abs/1801.09710) | [[~~code~~]]() | |
| **Parallel WaveGAN** | *A fast waveform generation model based on GAN with multi-resolution spectrogram* | [[arXiv]](https://arxiv.org/abs/1910.11480) | [[~~code~~]]() | |
| **WaveGAN** | *Synthesizing Audio with Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1802.04208) | [[~~code~~]]() | |
## Applied Others
Here for the GAN applications on other domains,
like nlp, tabular, etc.
| *Name* | *Summary* | *Paper* | *Code* | *Official Code* |
|:----------------:|:----------------------------------------------------------------------:|:-----------------------------------------------:|:----------------------:|:---------------:|
| **AnoGAN** | *Unsupervised Anomaly Detection with Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1703.05921) | [[~~code~~]](./AnoGAN) |
| **CipherGAN** | *Unsupervised Cipher Cracking Using Discrete GANs* | [[github]](https://arxiv.org/abs/1801.04883) | [[~~code~~]]() |
| **DiscoGAN** | *Discover Cross-Domain Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1703.05192) | [[~~code~~]]() |
| **eCommerceGAN** | *A Generative Adversarial Network for E-commerce* | [[arXiv]](https://arxiv.org/abs/1801.03244) | [[~~code~~]]() |
| **PassGAN** | *A Deep Learning Approach for Password Guessing* | [[arXiv]](https://arxiv.org/abs/1709.00440) | [[~~code~~]]() |
| **SeqGAN** | *Sequence Generative Adversarial Networks with Policy Gradient* | [[arXiv]](https://arxiv.org/abs/1609.05473) | [[~~code~~]]() |
| **TAC-GAN** | *Text Conditioned Auxiliary Classifier Generative Adversarial Network* | [[arXiv]](https://arxiv.org/abs/1703.06412.pdf) | [[~~code~~]]() |
## Useful Resources
Here for the useful resources when you try to train and stable a gan model.
| *Name* | *Summary* | *Link* |
|:---------:|:--------------------------------------------:|:---------------------------------------------:|
| GAN Hacks | a bunch of tips & tricks to train GAN stable | [github](https://github.com/soumith/ganhacks) |
## Note
Any suggestions and PRs and issues are WELCOME :)
## Author
HyeongChan Kim / [@kozistr](http://kozistr.tech)