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