This repository provides implementations and resources for various Generative Adversarial Network (GAN) architectures, including GAN, DCGAN, WGAN, CGAN, and InfoGAN. It is designed for learning and understanding the mathematical underpinnings and practical coding of these models, targeting individuals interested in deep learning and generative modeling.

How It Works

The project implements classical GAN variants with simplified network structures, primarily tested on the MNIST dataset. It focuses on demonstrating how mathematical concepts like network architecture and loss functions translate into code. The implementations cover architectural improvements for stability (DCGAN), conditional generation (CGAN), Wasserstein distance for improved training stability (WGAN), and unsupervised latent space discovery (InfoGAN).

Quick Start & Requirements

• Install: Python 2.7+ and TensorFlow 1.0+.
• Dependencies: No specific hardware like GPUs are mentioned as mandatory, but performance will be significantly impacted.
• Setup: Assumed to be straightforward for users familiar with Python and TensorFlow 1.x.
• Resources: Links to external blogs and repositories for theoretical details and alternative implementations are provided.

Highlighted Details

• Implements and compares multiple GAN variants: GAN, DCGAN, WGAN, CGAN, InfoGAN.
• Demonstrates architectural choices like convolutional layers, batch normalization, and activation functions (ReLU, Tanh, LeakyReLU).
• Explores different loss functions and training strategies, including weight clipping and step ratios between generator and discriminator.
• Includes sample generated images for each implemented model to showcase results.

Maintenance & Community

The repository is maintained by yfeng95. No specific community channels or active development signals are present in the README.

Licensing & Compatibility

The README does not explicitly state a license. Given the nature of the code and its dependencies, it is likely intended for educational and research purposes. Commercial use would require careful review of TensorFlow's licensing and any other included libraries.

Limitations & Caveats

The code is tested with older versions of Python (2.7) and TensorFlow (1.0+), which are now deprecated and may not be compatible with current environments. The implementations are simplified and primarily tested on MNIST, which might limit direct applicability to more complex datasets or tasks without significant modification.