What does "Mutual Distillation" mean?
Table of Contents
Mutual distillation is a method used to improve the performance of models that work together. In this process, different models, or "experts," share knowledge with each other to enhance their understanding and skills.
How It Works
Each expert focuses on specific tasks, but sometimes their view can be too narrow. By using mutual distillation, experts can learn from one another. This means that when one expert finds useful information, it can share that with others. This sharing helps all the experts perform better in their own tasks.
Benefits
The main advantage of mutual distillation is that it allows models to combine their strengths. As they learn from each other, they become more skilled and can handle a wider range of situations. This leads to an overall improvement in performance, making the system more effective.
Applications
Mutual distillation can be applied in various areas, such as analyzing data, processing language, and recognizing images. It is useful for enhancing the capabilities of models in many different fields.