What is knowledge distillation?

What is knowledge distillation?

Knowledge distillation is a technique in machine learning where a smaller model is trained to replicate the behavior of a larger, more complex model.

Why is knowledge distillation important?

It's important because it allows for the deployment of smaller models that are faster and require less computational resources while maintaining performance.

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

Sure! For instance, a large neural network can be distilled into a smaller one that can run efficiently on mobile devices.

What are the challenges of knowledge distillation?

Some challenges include ensuring that the distilled model retains the accuracy of the original and managing the trade-off between size and performance.