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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 13·5 min read
CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification
TL;DR
CLEAR introduces a modular ensemble framework for long-tailed classification that enhances model reliability by aggregating class-wise expert predictions based on trust scores. This approach significantly improves accuracy, especially in few-shot scenarios, across various datasets.
✦ Why It Matters
Implement CLEAR to improve classification accuracy on imbalanced datasets in your projects today.
Key Takeaways
- Utilize CLEAR for better handling of imbalanced datasets.
- Implement structured sampling to create diverse experts.
- Calculate class-wise trust scores for expert predictions.
- Focus on few-shot performance improvements in your models.
How It Works
CLEAR generates diverse experts through threshold-based structured sampling, ensuring that all classes are represented. It then assesses the reliability of each expert for specific classes by calculating class-wise trust scores based on smoothed precision.
During inference, it combines predictions from different experts using a class-wise generalized product-of-experts approach, allowing the model to leverage the most reliable experts for each class.