Welcome to this guide on utilizing the Keras-GAN repository, which features a collection of Keras implementations of Generative Adversarial Networks (GANs) presented in research papers. While the repository has recently become unmaintained, you’ll find valuable insights and simplified implementations of various GAN models. Let’s dive right in!

Table of Contents

• Installation
• Implementations

Installation

Setting up the Keras-GAN repository is straightforward. Follow these commands in your terminal:

$ git clone https://github.com/eriklindernoren/Keras-GAN 
$ cd Keras-GAN 
$ sudo pip3 install -r requirements.txt

Implementations

The Keras-GAN repository features various GAN implementations. Below is a brief overview of some key models:

1. AC-GAN

Implementation of Auxiliary Classifier Generative Adversarial Network.

Code: Paper

$ cd acgan 
$ python3 acgan.py

2. CycleGAN

Uses cycle-consistency for unpaired image-to-image translation.

Code: Paper

$ cd cyclegan 
$ bash download_dataset.sh apple2orange 
$ python3 cyclegan.py

3. Pix2Pix

Image-to-image translation utilizing conditional GANs.

Code: Paper

$ cd pix2pix 
$ bash download_dataset.sh facades 
$ python3 pix2pix.py

Troubleshooting

It’s possible to run into issues while implementing these GAN models. Here are some troubleshooting ideas:

• Dependencies: Ensure all dependencies in requirements.txt are installed correctly. Run pip3 install -r requirements.txt again if you encounter issues.
• Dataset Download: If the dataset doesn’t download properly, check the internet connection or manually download the dataset using the links provided in the respective model’s section.
• Python Version: Make sure you are using Python 3 for these scripts, as older versions may lead to compatibility issues.
• Model Performance: If the models do not perform as expected, consider checking your system’s GPU settings or experiment with different hyperparameters.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.