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Active Learning for Data Efficiency
Active learning is a machine learning paradigm where the algorithm intelligently selects specific unlabeled examples to be labeled by a human annotator, instead of randomly sampling. The goal is to achieve high model accuracy with minimal labeling effort, by querying the most informative data points. This often involves techniques like uncertainty sampling or query-by-committee.
In plain terms
It's like a smart student who only asks the teacher questions about the problems they are most unsure about, rather than asking about every single problem.
Why it matters
This significantly reduces the cost and time associated with obtaining labeled data, especially in domains where labeling is expensive.
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