Semi-Supervised and Self-Training

Semi-supervised learning uses a small labeled set plus a large unlabeled pool. Self-training labels the unlabeled data with the model's own confident predictions and retrains; consistency regularization forces the model to give the same answer to perturbed copies of an input. It helps when labels are scarce but unlabeled data is plentiful and the cluster assumption holds, and it backfires through confirmation bias when the model is wrong but confident.