What is few-shot learning and why is it useful?
Few-shot learning shows that sometimes less really is more.
How It Works:
Few-shot learning leverages pre-trained models that adapt to new tasks using only a handful of labeled examples, by generalizing patterns learned during initial training.
Key Benefits:
- Data efficiency: Requires minimal annotated samples.
- Rapid prototyping: Spin up new tasks in hours, not weeks.
- Cost savings: Reduces labeling expenses.
Real-World Use Cases:
- Custom intent classification: Add new customer intents with 5-10 examples.
- Medical imaging: Classify rare pathologies from limited scans.
FAQs
How few is "few"?
Does performance match full-data models?
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