Semi-supervised-learning

Why we included this project

When you have plenty of unlabeled data but only a handful of labeled examples, semi-supervised learning can get you surprisingly far. This codebase bundles many of the best-known SSL algorithms, like FixMatch and FlexMatch, into one consistent PyTorch pipeline, so you don't have to dig through each paper's messy reference implementation to compare them. It was built around a NeurIPS 2022 benchmark and covers vision, text, and audio classification through a shared API, which makes swapping methods, backbones, and datasets painless. The benchmark results are documented and reproducible out of the box, which is a big time-saver whether you're looking for a baseline to beat or a reliable starting point for your own low-resource classifier.