Learning with not Enough Data Part 2: Active Learning
📰 Lilian Weng's Blog
Active learning helps with limited labeled data by selectively labeling samples under a limited budget
Action Steps
- Identify the most informative samples for labeling
- Use uncertainty measures or diversity metrics to select samples
- Annotate the selected samples and update the model
- Repeat the process until the labeling budget is exhausted
Who Needs to Know This
Data scientists and machine learning engineers can benefit from active learning to improve model performance with limited labeling resources, and product managers can use this to optimize data collection budgets
Key Insight
💡 Active learning can significantly improve model performance with limited labeling resources by focusing on the most informative samples
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📊 Boost model performance with active learning! Selectively label samples to maximize improvement
Key Takeaways
Active learning helps with limited labeled data by selectively labeling samples under a limited budget
Full Article
<!-- The performance of supervised learning tasks improves with more high-quality labels available. However, it is expensive to collect a large number of labeled samples. Active learning is one paradigm to deal with not enough labeled data, when there are resources for labeling more data samples but under a limited budget. --> <p>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 involv
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