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Related Questions
- What are the key challenges in transferring knowledge from a pre-trained model to a smaller model during knowledge distillation?
- How does the temperature parameter in knowledge distillation affect the accuracy of the distilled model?
- What are the common pitfalls in fine-tuning a pre-trained model for a specific task, and how can they be avoided?
- Can knowledge distillation be used for transfer learning in multi-task learning scenarios, and if so, how?
- What are the differences between knowledge distillation and other model compression techniques, such as pruning and quantization?
- How does the choice of distillation temperature and other hyperparameters impact the performance of the distilled model?
- What are the limitations of knowledge distillation in terms of preserving the representational power of the original model?
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