Semi-supervised-learning and TorchSSL
These are competitors offering overlapping PyTorch-based frameworks for semi-supervised learning with similar scope (unified codebases implementing multiple SSL algorithms), though Microsoft's version is slightly more recent (NeurIPS'22 vs '21) and marginally more starred.
About Semi-supervised-learning
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.
About TorchSSL
TorchSSL/TorchSSL
A PyTorch-based library for semi-supervised learning (NeurIPS'21)
This project provides a collection of established methods for semi-supervised learning. It takes in datasets where only a small portion of the data is labeled and a larger portion is unlabeled, then outputs trained machine learning models that can classify new, unseen data. Researchers and practitioners working on computer vision tasks who need to build classification models with limited labeled data would find this useful.
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