An awesome-list repository that compiles research papers, methods, and educational resources on semi-supervised learning. It covers approaches including generative mixture models, self-training, consistency regularization, co-training, and graph-based methods. The list serves as a reference for researchers and practitioners working with machine learning techniques that leverage unlabeled data alongside labeled examples.