Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
📰 ArXiv cs.AI
Learn how to efficiently address class imbalance in real-world datasets using active learning with foundation model priors, improving model performance on minority classes
Action Steps
- Build a foundation model prior to address class imbalance
- Run active learning algorithms to selectively query informative samples
- Configure the framework to prioritize minority classes
- Test the framework on real-world datasets with skewed class distributions
- Apply the approach to various domains such as image and text classification
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this approach to improve model performance, while product managers can utilize it to enhance overall product quality
Key Insight
💡 Active learning with foundation model priors can efficiently mitigate class imbalance in real-world datasets
Share This
🚀 Improve model performance on minority classes with active learning and foundation model priors! 📊
DeepCamp AI