Learning with not Enough Data Part 2: Active Learning
lilianweng.github.io- Category
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- ARTICLE
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- @lilianweng
- Added
- Jul 21, 2026
About
This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but within a budget limit, and therefore we need to be smart when selecting which samples to label.
Why it made the leaderboard
If you're training models with scarce labels and a tight annotation budget, this walks through active learning methods for choosing which samples are worth labeling — a practical lever for maximizing model quality per dollar of human labeling.
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