Hello everyone, I am Wei Xue AI. Today I will introduce to you the application of deep learning techniques 31 - the practical application of knowledge distillation technology on the convolutional residual network ResNet, and load the real data set for distillation training. Knowledge distillation for model compression is a model compression technology that achieves model compression by migrating the knowledge of a large model (teacher model) to a small model (student model). This approach can be used to reduce model size while maintaining high accuracy.
Table of contents
1. Why knowledge distillation is needed
2. The process of knowledge distillation
3. The method of knowledge distillation
4. Mathematical principles of knowledge distillation
5. Case: teacher model vs student model
6. Code implementation of knowledge distillation techniques
7. Summary
1. Why knowledge distillation is necessary
In deep learning, a large model (teacher model) is usually trained to achieve high accuracy. However, such models are usually very large and require a lot of computing resources and storage space. Knowledge distillation provides a way to reduce resource requirements by training a smaller model (student model) to imitate the behavior of a large model.
2. The process of knowledge distillation
Train the teacher model: First train a large model (teacher model) to obtain high accuracy.
Extract knowledge: predict the input data through the teacher model, and store the prediction results as "knowledge" in a new data set.
Train the student model: Train a smaller model (the student model) using the knowledge extracted from the teacher model as labels.
Make predictions: Use student model to make predictions