Semi-Supervised Learning
Semi-supervised learning is a training paradigm that uses both labeled and unlabeled data for training. It's particularly useful when obtaining labeled data is expensive or time-consuming, but large amounts of unlabeled data are readily available. The model often learns initial patterns from the small labeled dataset and then uses those patterns to make sense of the larger unlabeled dataset, iteratively improving its understanding.
Imagine teaching a child to recognize animals by showing them a few pictures with names (labeled data), and then letting them look at a large pile of animal pictures without names (unlabeled data), inferring the labels based on what they already know.
This approach allows AI systems to achieve higher performance with less human effort in labeling data, making many real-world applications more feasible.
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