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Related Questions
- How does fine-tuning on pre-trained models leverage domain knowledge compared to random initialization?
- What are the key performance differences between fine-tuning and random initialization in deep learning models?
- Can you explain the role of pre-trained models in reducing the risk of overfitting in fine-tuning?
- How does fine-tuning on pre-trained models impact the training time and computational resources compared to random initialization?
- What are some common applications where fine-tuning on pre-trained models outperforms random initialization?
- Can you discuss the trade-offs between fine-tuning on pre-trained models and random initialization in terms of model flexibility and adaptability?
- How does fine-tuning on pre-trained models affect the interpretability of the learned models compared to random initialization?
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