In Depth
Semi-supervised learning occupies the middle ground between supervised learning (all data labeled) and unsupervised learning (no labels). It leverages a small labeled dataset alongside a much larger unlabeled dataset, using the structure and patterns in the unlabeled data to improve the model's understanding beyond what the limited labels alone could provide.
Common techniques include pseudo-labeling (using the model's own predictions on unlabeled data as training labels), consistency regularization (ensuring the model produces similar outputs for augmented versions of the same input), and co-training (using multiple models to label data for each other). MixMatch and FixMatch are popular frameworks that combine several of these techniques.
Semi-supervised learning is particularly valuable in real-world scenarios where labeling data is expensive, time-consuming, or requires domain expertise. Medical imaging, industrial inspection, and specialized text classification are common applications where obtaining labeled examples is costly but unlabeled data is abundant. The approach can dramatically reduce the amount of labeled data needed to achieve strong performance.