1. Distillation_ "3, AI algorithm optimization"

Distillation

Transferring knowledge learned from a large, complex model (often called the teacher model) to a smaller, simpler model

Process: First, a complex teacher model is trained that performs well when processing large amounts of data. Then, the teacher model is used to generate "soft labels", which are the predicted probability distributions for each category. These soft labels not only contain category information, but also reflect the model's understanding of the relationship between different categories.

The student model is then trained using these soft labels and learns to imitate the output of the teacher model. Ultimately, the trained student model is able to achieve performance close to that of the teacher model on a specific task with fewer parameters.

Application: Distillation technology is widely used in resource-constrained environments such as mobile devices and edge computing, enabling complex deep learning models to run efficiently on these devices while ensuring response speed and accuracy.