microsoft/Semi-supervised-learning
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)
This project helps machine learning developers efficiently train models for image, text, or audio classification tasks when they only have a small amount of labeled data, but access to a lot of unlabeled data. It takes in both labeled and unlabeled datasets and outputs a high-performing classification model. Machine learning engineers and researchers in computer vision, natural language processing, or audio analysis will find this useful.
1,563 stars.
Use this if you need to build robust classification models but are limited by the cost or time required to meticulously label large datasets.
Not ideal if you have abundant labeled data for your task, as supervised learning methods might be more straightforward.
Stars
1,563
Forks
213
Language
Python
License
MIT
Category
Last pushed
Jan 07, 2026
Commits (30d)
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