Semi-supervised learning (SSL) reduces the dependency on labeled data by utilizing a combination of labeled and unlabeled data to improve model training. In many real-world scenarios, obtaining a fully labeled dataset can be time-consuming and expensive. SSL addresses this issue by leveraging the vast amounts of unlabeled data that are often more abundant. By incorporating both labeled data for initial training and unlabeled data to refine the model, SSL can achieve better performance without requiring extensive labeling efforts.
One of the key ways SSL accomplishes this is through techniques such as data augmentation and consistency training. For example, a model may be trained on a small number of labeled images, but during training, it can process variations of the same images (like different rotations, scales, or colors) as unlabeled data. The idea is that the model should produce consistent outputs regardless of these transformations, encouraging it to learn robust features of the data. This approach effectively enhances the volume of input data without needing to label each instance explicitly.
Furthermore, SSL often includes clustering methods to organize the unlabeled data. For instance, a model can group similar unlabeled instances and then assign pseudo-labels based on the majority class of these groups. This way, the model learns from not just the labeled examples but also from unlabeled examples that it believes are similar. Consequently, SSL allows developers to create more accurate models with fewer labeled samples, making it a practical solution when labeled data is scarce or expensive to obtain.