What is knowledge distillation in machine learning?

What is knowledge distillation in machine learning?

Knowledge distillation is a technique where a smaller model (student) is trained to replicate the behavior of a larger model (teacher).

Why is knowledge distillation important?

It's important because it allows for the deployment of smaller, more efficient models without significant loss in performance.

Can you give an example of how it's used?

Sure! It's often used in scenarios like mobile applications where computational resources are limited, yet high accuracy is needed.

What are the challenges of implementing knowledge distillation?

Challenges include ensuring the student model captures the teacher's knowledge effectively and managing the trade-off between size and accuracy.