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Related Questions
- What are the primary goals of knowledge distillation in machine learning?
- How does knowledge distillation differ from model pruning in terms of data and computational requirements?
- What are the benefits of using knowledge distillation over other model compression techniques such as quantization?
- Can you explain the concept of 'teacher' and 'student' models in knowledge distillation?
- How does knowledge distillation impact the accuracy of a compressed model compared to its uncompressed counterpart?
- What are some common applications of knowledge distillation in real-world scenarios?
- How does knowledge distillation relate to other model compression techniques, such as pruning and quantization, in terms of trade-offs between model size and accuracy?
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