This repository serves as a curated list of research papers and associated code related to Generative Adversarial Networks (GANs). It targets researchers, engineers, and students in machine learning and computer vision, providing a structured overview of GAN applications, theoretical advancements, and training techniques. The primary benefit is a centralized, categorized resource for exploring the rapidly evolving field of GANs.

How It Works

The repository organizes papers by application domain (e.g., Image Translation, Facial Attribute Manipulation, Super-Resolution) and theoretical aspects (e.g., GAN Theory, Training Techniques). Each entry typically includes links to the original paper and available code implementations, facilitating direct access to foundational research and practical codebases. This categorization allows users to efficiently navigate and discover relevant GAN research.

Quick Start & Requirements

This repository is a curated list and does not require installation or execution. It serves as a reference guide.

Highlighted Details

• Comprehensive coverage of GAN applications across various computer vision tasks.
• Links to seminal papers and their code implementations, including foundational works like "Generative Adversarial Nets" (NIPS 2014).
• Categorization extends to theoretical aspects, training methods, and interdisciplinary applications.
• Includes links to tutorials and blog posts for deeper understanding.

Maintenance & Community

The repository is maintained by zhangqianhui. No specific community channels or active development signals are immediately apparent from the README.

Licensing & Compatibility

The repository itself is a list of links and does not impose a license. The licenses of the linked papers and code repositories vary and must be checked individually.

Limitations & Caveats

This is a curated list of papers and code, not an executable library. The availability and quality of linked code can vary, and some links may become outdated. The "heavy_check_mark" indicators suggest paper/code availability but do not guarantee functionality or completeness.