Awesome Generative Adversarial Networks with tensorflow
Awesome Generative Adversarial Networks with tensorflow
Tensorflow implementation of GANs (Generative Adversarial Networks)
tf 2.x.Maybe some codes wouldn't work on master branch, because i'm just working on the branch.
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' :).
Now on refactoring... All GAN training script can be run module-wisely like below. (WIP)
You can also use conda, virtualenv environments.
$ python3 -m pip install -r requirements.txtBefore running the model, make sure that
awesome_gans/config.py, there are several configurations, customize with your flavor!$ python3 -m awesome_gans.acganSupporting datasets are ... (code is in /awesome_gans/datasets.py)
…Here's the list-up for tons of GAN papers. all papers are sorted by alphabetic order.
Here's the beginning of the GAN.
| Name | Summary | Paper | Code |
|---|---|---|---|
| GAN | Generative Adversarial Networks | [arXiv] | [code] |
Here for the theories & concepts of the GAN.
| Name | Summary | Paper | Code | Official Code |
|---|---|---|---|---|
| ACGAN | Auxiliary Classifier Generative Adversarial Networks | [arXiv] | [code] | |
| AdaGAN | Boosting Generative Models | [arXiv] | [ |
|
| bCR | Improved Consistency Regularization for GANs | [arXiv] | [ |
|
| BEGAN | Boundary Equilibrium Generative Adversarial Networks | [arXiv] | [code] | |
| BGAN | Boundary-Seeking Generative Adversarial Networks | [arXiv] | [code] | |
| BigGAN | Large Scale GAN Training for High Fidelity Natural Image Synthesis | [arXiv] | [ |
|
| CGAN | Conditional Generative Adversarial Networks | [arXiv] | [code] | |
| CoGAN | Coupled Generative Adversarial Networks | [arXiv] | [code] | |
| ConSinGAN | Improved Techniques for Training Single-Image GANs | [WACV21] | [ |
[official] |
| DCGAN | Deep Convolutional Generative Adversarial Networks | [arXiv] | [code] | |
| DRAGAN | On Convergence and Stability of Generative Adversarial Networks | [arXiv] | [code] | |
| EBGAN | Energy-based Generative Adversarial Networks | [arXiv] | [code] | |
| EqGAN-SA | Improving GAN Equilibrium by Raising Spatial Awareness | [arXiv] | [ |
[official] |
| f-GAN | Training Generative Neural Samplers using Variational Divergence Minimization | [arXiv] | [code] | |
| GP-GAN | Towards Realistic High-Resolution Image Blending | [arXiv] | [ |
|
| Softmax GAN | Generative Adversarial Networks with Softmax | [arXiv] | [code] | |
| GAP | Generative Adversarial Parallelization | [arXiv] | [ |
|
| GEGAN | Generalization and Equilibrium in Generative Adversarial Networks | [arXiv] | [ |
|
| G-GAN | Geometric GAN | [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] | [code] | |
| MSGGAN | Multi-Scale Gradients for Generative Adversarial Networks | [arXiv] | [ |
|
| PGGAN |
No open issues yet, or sync has not completed.