AdversarialNetsPapers

Why we included this project

For anyone working with generative adversarial networks, this index saves the tedious part of research: finding the paper and then hunting down a working implementation. It reads like a guided path, opening with the original 2014 Generative Adversarial Nets paper and fanning out into application areas such as image translation, super-resolution, and video prediction. Each entry pairs the arXiv paper with its code repository, so you can move from reading a method to running it without a separate search. It is a reference resource rather than software you deploy, but the long-running curation is the real value: when you need a canonical paper and a working codebase together, the links are already there.