Semi-Supervised Learning
Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data during training. It leverages patterns found in the abundant unlabeled data to improve the model's understanding even when explicit answers are scarce. This often involves techniques like consistency regularization or pseudo-labeling, where the model itself generates labels for unlabeled examples.
Imagine teaching a child to identify animals, showing them a few labeled pictures and then letting them try to guess many more, subtly correcting their understanding as they go.
It significantly reduces the need for expensive, time-consuming manual data labeling, making AI applicable in data-scarce domains.
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