Knowledge Distillation

Knowledge distillation is a technique used to transfer knowledge from a larger model to a smaller model. The goal of knowledge distillation is to train the smaller model to mimic the behavior of the larger model on a specific task or dataset. There are several techniques used for knowledge distillation, including:

1. Temperature scaling: Scaling the temperature of the larger model to reduce its entropy and transfer knowledge to the smaller model.
2. Soft attention: Using soft attention to guide the smaller model to focus on the most important parts of the input and mimic the behavior of the larger model.
3. Gradient distillation: Using the gradients of the larger model to train the smaller model to mimic the behavior of the larger model.
4. Soft output distillation: Using soft output to guide the smaller model to mimic the behavior of the larger model.