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Related Questions
- How does the amount of labeled data affect the model's performance and the curation effort?
- What are the trade-offs between data quality, quantity, and diversity in terms of model performance and curation effort?
- Can you explain the concept of 'curate once, use many times' and how it impacts the balance between data curation effort and model performance?
- How does the choice of model architecture influence the required data curation effort and model performance?
- What are some strategies for reducing data curation effort while maintaining or improving model performance?
- Can you discuss the relationship between data curation effort and model interpretability?
- How does the use of transfer learning affect the balance between data curation effort and model performance?
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