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Related Questions
- What are the key factors that influence the choice of pre-training task and dataset for a downstream task?
- Can you explain the relationship between the choice of pre-training task and the level of fine-tuning required for a given dataset?
- How does the size and quality of the pre-training dataset impact the trade-off between fine-tuning and training from scratch?
- What are the implications of choosing a pre-training task that is unrelated to the downstream task for the performance of a fine-tuned model?
- Can you discuss the role of transfer learning in the context of pre-training and fine-tuning, and how it affects the trade-off between the two approaches?
- What are the potential risks and benefits of reusing a pre-trained model's weights for a new downstream task, and how do they impact the trade-off between fine-tuning and training from scratch?
- How does the choice of pre-training task and dataset influence the need for domain adaptation in fine-tuning, and what are the implications for model performance?
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