Keras-GAN

This repository is a collection of Python implementations of 21 different Generative Adversarial Network architectures, all written using Keras, a popular deep learning library. Generative Adversarial Networks, or GANs, are a type of AI model where two neural networks are trained together: one generates fake data (usually images), and the other tries to distinguish real data from fake. Over many training rounds, the generator gets better at producing convincing output.

The 21 implementations here each correspond to a research paper and cover a range of GAN variations with different goals. Some are designed to generate images that match a certain style or category. CycleGAN, for example, can translate photos of apples into oranges and back without needing paired example images. Pix2Pix translates images of one type into another, such as turning building facades sketches into photographs. SRGAN (super-resolution GAN) attempts to increase the resolution of a low-quality image. Other variants explore different training techniques that stabilize or improve the generation process.

Each implementation lives in its own subfolder and includes a short description, a link to the original research paper, and a command you can run to start training. Most examples train on image datasets that are either built into the code or downloadable via included scripts.

The README notes that the project is no longer actively maintained, and the implementations are described as simplified versions of the original papers: they aim to convey the core concept of each approach rather than reproduce every architectural detail exactly.

This repository suits someone learning about how GANs work by reading and running code, or a researcher who wants a quick reference implementation to compare against or build from. A related repository using PyTorch instead of Keras is also linked.