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AI Research
arXiv CS.AI·Jun 9·5 min read
Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
✦ Why It Matters
Engineers can implement this active learning framework to enhance model performance on imbalanced datasets effectively.
Key Takeaways
- Utilize foundation model priors for better sample selection.
- Reduce annotation costs by over 50% with this framework.
- Address both class imbalance and label noise effectively.
- Enhance model robustness while minimizing data annotation efforts.
How It Works
The proposed framework combines insights from a large foundation model with a smaller model to make informed decisions about which samples to annotate. By focusing on the most informative and balanced samples, it mitigates the effects of class imbalance and label noise, leading to more efficient learning.