Semi-supervised learning combines a small set of labeled examples with a much larger pool of unlabeled data during training. The idea is straightforward: labeling thousands of images costs time and money, so the algorithm should extract as much signal as possible from the unlabeled majority while using the labeled subset as an anchor for learning correct predictions.

A typical pipeline works in two stages. First, a model trains on the labeled portion and then generates pseudo-labels for the unlabeled images. Confident predictions become part of the training set, and the model retrains on the expanded dataset. Techniques like FixMatch and MixMatch add consistency regularization, requiring the model to produce the same output regardless of how an image is augmented. This pushes the decision boundary into low-density regions of the data space.

Semi-supervised methods are especially useful when you have thousands of raw images but can only afford to annotate a few hundred. Object detection and medical imaging workflows benefit heavily, since expert annotation is the main bottleneck. In production, teams often label 10-20% of their data manually, apply semi-supervised training to reach acceptable accuracy, then selectively label hard examples to push performance further.