Semi-Supervised Learning

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Semi-supervised learning (SSL) is a hybrid approach that combines labeled and unlabeled data. When getting labels is expensive or time-consuming, the model uses the smaller set of labeled examples to learn the main patterns, and then leverages the larger pool of unlabeled data to refine its understanding. For example, in image classification, a few hundred labeled photos can guide the model while thousands of unlabeled images help it recognize variations and generalize better.

What sets SSL apart from purely supervised or unsupervised learning is that it learns from both types of data at once. It’s widely used in vision, NLP, and other areas where unlabeled data is plentiful but human labeling is costly, often improving generalization beyond using labeled data alone.

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