SSL (Semi-Supervised Learning) addresses overfitting by leveraging both limited labeled data and abundant unlabeled data to improve generalization. Overfitting occurs when a model memorizes training data patterns instead of learning generalizable features, leading to poor performance on unseen data. SSL mitigates this by using unlabeled data to uncover broader data structures, reducing reliance on small labeled datasets. For example, in text classification, a model trained on a few labeled emails and many unlabeled ones might learn word distributions common across both spam and legitimate messages. This broader understanding helps the model avoid fixating on noisy or sparse patterns in the limited labeled examples.
A key SSL strategy is consistency regularization, which enforces stability in predictions when inputs are perturbed. This technique trains models to produce similar outputs for slightly altered versions of the same data, such as adding noise to images or paraphrasing text. For instance, in image recognition, applying random crops or rotations to unlabeled images and requiring consistent predictions teaches the model to focus on invariant features (e.g., shapes) rather than incidental details (e.g., background). Similarly, in NLP, models trained to predict the same sentiment for paraphrased sentences learn to ignore superficial wording changes. This reduces overfitting by making the model robust to irrelevant variations.
SSL also uses pseudo-labeling, where the model generates labels for unlabeled data and retrains on high-confidence predictions. This expands the effective training set, exposing the model to more diverse examples. For example, a speech recognition model might label unspoken audio clips with predicted transcripts, then retrain using these pseudo-labels alongside human-labeled data. To avoid reinforcing errors, confidence thresholds or ensemble methods (e.g., averaging predictions from multiple models) filter unreliable pseudo-labels. Additionally, entropy minimization encourages the model to make decisive predictions on unlabeled data, smoothing decision boundaries. These techniques collectively balance the use of scarce labeled data and plentiful unlabeled data, reducing overfitting while maintaining accuracy.