#Semi-Supervised Learning

This compilation provides comprehensive materials on semi-supervised learning, aimed at researchers and practitioners interested in using unlabeled data to enhance classification models. It includes diverse methods like EM mixture models and graph-based strategies integrated into deep learning frameworks for efficient and accurate model training. Covering applications in computer vision, NLP, and reinforcement learning, this resource supports theoretical and practical advancements in AI. Community contributions are encouraged for ongoing updates.

TorchSSL, a now deprecated PyTorch toolkit for semi-supervised learning (SSL), historically contributed to SSL research by implementing 11 pivotal algorithms like FlexMatch. Supporting key datasets such as CIFAR-10 and ImageNet, it offered comprehensive comparison capabilities. Despite transitioning to USB for improved efficiency, TorchSSL's configurations, logs, and model weights remain valuable for understanding SSL advancements in fields like computer vision and NLP.

The Transfer-Learning-Library (TLlib) is a high-performance PyTorch-based resource designed for Transfer Learning. It features a comprehensive API for methods including domain alignment, translation, and self-training. Ideal for implementing or applying algorithms across classification, regression, and other tasks, TLlib aligns with torchvision's structure, facilitating easy development of transfer learning solutions. Extensive documentation supports various learning setups such as domain and task adaptation, enhancing deep learning research objectively.