Learning with not Enough Data Part 1: Semi-Supervised Learning
📰 Lilian Weng's Blog
Semi-supervised learning utilizes a large amount of unlabeled data and a small amount of labeled data to improve performance when labels are scarce
Action Steps
- Identify supervised learning tasks with limited labeled data
- Explore semi-supervised learning as an alternative approach
- Collect a large amount of unlabeled data to supplement the limited labeled data
- Implement semi-supervised learning algorithms to improve model performance
Who Needs to Know This
Data scientists and machine learning engineers on a team can benefit from semi-supervised learning to improve model performance with limited labeled data, and product managers can understand the potential of this approach to reduce data collection costs
Key Insight
💡 Semi-supervised learning can improve model performance when labeled data is limited
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🤖 Semi-supervised learning: using unlabeled data to boost model performance when labels are scarce!
Key Takeaways
Semi-supervised learning utilizes a large amount of unlabeled data and a small amount of labeled data to improve performance when labels are scarce
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. There are several paradigms in machine learning to deal with the scenario when the labels are scarce. Semi-supervised learning is one candidate, utilizing a large amount of unlabeled data conjunction with a small amount of labeled data. --> <p>When facing a limited amount of labeled data for supervised learning tasks, four appr
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