Overview
Papers With Code is a platform that links academic papers directly to open-source implementations, helping users quickly find the code, datasets, and evaluation results associated with a paper. The site aggregates the latest machine learning/AI papers, GitHub implementations, benchmarks, and leaderboards, emphasizing reproducibility and comparative performance.
Key features
- Fast search: find implementations by task, model, dataset, or paper.
- Code links: jump directly to
GitHubor authors' implementations to facilitate reproduction and further development. - Leaderboards: compare different methods on standard benchmarks and track SOTA progress.
- Data and reproduction notes: provide dataset links, evaluation protocols, and details of reproduction experiments.
Use cases and target users
Suitable for researchers, engineers, students, and product managers for literature review, reproducing experiments, quickly getting started with model implementations, or evaluating the latest methods. Whether compiling surveys, running comparative experiments, or looking for usable open-source implementations, it significantly improves efficiency.
Main advantages or highlights
- A centralized, continuously updated resource that shortens the path from paper to implementation;
- Community-driven and open-source focus, making comparison, reproduction, and collaboration easier;
- Clear leaderboards and benchmark information to help users quickly judge a method's strengths and applicable scenarios.