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Related Questions
- What are the key challenges in fine-tuning pre-trained models for specific downstream tasks?
- How do pre-trained models leverage domain knowledge and learn generalizable representations?
- What are some common techniques used to adapt pre-trained models to new tasks and domains?
- Can you discuss the role of transfer learning in reducing the need for large amounts of labeled training data?
- How do pre-trained models handle out-of-distribution data and what are some strategies to mitigate this?
- What is the relationship between pre-training objectives and the performance of pre-trained models on specific tasks?
- Can you explain the difference between feature-based and learning-based transfer learning approaches?
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