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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Awesome Generative Adversarial Networks with tensorflow
| Date | Stars |
|---|---|
| 2026-07-24 | 758 |
| 2026-07-25 | 758 |
| 2026-07-28 | 758 |
| 2026-07-30 | 758 |
| 2026-08-06 | 758 |
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# 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* | |:-----------------:|:------------------------------------------------------------------------------------------
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Hyeongchan Kim · South Korea
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6a9f16bbefec926d, topic:tensorflow
matched fp:6a9f16bbefec926d, topic:gan