Over the past few years, I’ve been re-implementing computer vision papers in PyTorch to understand their core mechanisms.

Each implementation is intentionally kept small and self-contained. The goal is not to provide production-ready training code, but to expose the essential ideas of each method once most abstractions and engineering details are removed.

The repository currently includes more than 50 implementations spanning: – Generative models (GANs, VAEs, diffusion) – 3D reconstruction and neural rendering – Meta-learning and representation learning

Design choices: – Minimal, single-file implementations – Code that stays close to the structure of the original papers – Emphasis on conceptual correctness over training scale or benchmarks – Reproduction of key figures or results when feasible

Repository: https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Cod...

I’d be interested in feedback from people who have implemented or reviewed these methods, particularly where this minimal approach oversimplifies important details.