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Related Questions
- Can you provide examples of industries where collecting and labeling data from scratch is more beneficial than using pre-trained models?
- In what situations does the lack of domain-specific knowledge in pre-trained models lead to inaccurate results?
- How do the costs of data collection and labeling compare to the costs of using pre-trained models in certain industries?
- What are the scenarios where the need for high accuracy and precision outweighs the convenience of using pre-trained models?
- Can you describe a case study where collecting and labeling data from scratch resulted in better performance than using a pre-trained model?
- How do the trade-offs between data quality, quantity, and annotation effort impact the decision to collect and label data from scratch?
- What are the potential consequences of using pre-trained models in industries with unique or proprietary data, and is collecting and labeling data from scratch a better alternative?
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