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Related Questions
- How does the choice of loss function during fine-tuning affect the model's generalizability to unseen data?
- Can you explain the relationship between the fine-tuning objective and the model's ability to adapt to new task domains?
- How does the choice of fine-tuning objective impact the model's robustness to out-of-distribution data?
- What are the differences in model generalizability when fine-tuning for classification versus regression tasks?
- In what ways does the fine-tuning objective influence the model's ability to handle concept drift in out-of-distribution data?
- Can you discuss the trade-offs between fine-tuning objectives in terms of model generalizability and task-specific performance?
- How does the choice of fine-tuning objective impact the model's ability to transfer knowledge across different task domains?
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