This repository serves as a comprehensive catalog of resources for understanding and implementing Generative Adversarial Networks (GANs). It targets researchers, students, and practitioners interested in the field of generative modeling, offering a curated collection of papers, code implementations, tutorials, and datasets to facilitate learning and development.

How It Works

The project focuses on the core GAN architecture, which involves a Generator network creating synthetic data and a Discriminator network distinguishing it from real data. This adversarial process drives the Generator to produce increasingly realistic outputs. The repository highlights various GAN architectures and their advancements, particularly in image generation, by providing links to papers and corresponding code implementations in popular frameworks like PyTorch and TensorFlow.

Quick Start & Requirements

• Install/Run: No specific installation or run commands are provided, as this is a resource repository. Users will need to clone repositories linked within the README to run specific implementations.
• Prerequisites: Python, PyTorch, TensorFlow, Keras, and potentially specific libraries mentioned in individual project repositories.
• Resources: Links to papers, code repositories, tutorials, blogs, talks, and datasets are provided.

Highlighted Details

• Extensive list of seminal and recent GAN papers with links to PyTorch, TensorFlow, and Keras implementations.
• Curated tutorials, blogs, and talks, including a NIPS 2016 tutorial by Ian Goodfellow.
• Links to popular datasets like CelebA, MNIST, ImageNet, and COCO for training and evaluation.
• Includes resources for advanced topics like CycleGAN, StarGAN, BigGAN, and SeqGAN.

Maintenance & Community

• The repository is maintained by savan77.
• Contributions are welcomed via pull requests or direct email to vsavan7@gmail.com.

Licensing & Compatibility

• The repository itself does not specify a license. Individual code implementations linked within the README will have their own licenses, which users must consult.

Limitations & Caveats

This repository is a curated list of resources and does not contain runnable code itself. Users must navigate to external links for implementations and datasets, and are responsible for managing dependencies and setup for each individual project.