Teacher-Gradient Adaptive Distillation for Fast, Precise Student Models
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Solution Overview
Problem
Deep learning models with large parameter quantities have high precision but slow prediction speed and high memory usage, while models with small parameter quantities have fast speed but low precision, and there is a need to improve the precision of low-precision student models while maintaining prediction speed during distillation training.
Innovation Solution
A model training method that involves inputting training samples with ground-truth labels into both a student and teacher model, determining losses between their predictions and the ground-truth labels, and adjusting the student model's parameters using a total loss function that incorporates an adaptive parameter related to the teacher model's gradient to dynamically adjust the amplitude of the distillation loss, ensuring stability and consistency in the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model with large parameter quantity is used, then precision is improved, but prediction speed decreases and memory occupation increases
Solution Approach 1:
The patent creates a student model that copies the knowledge and prediction patterns from a teacher model with large parameters. The student model learns by mimicking the teacher model's outputs through distillation loss, achieving high precision with fewer parameters and faster inference speed.
Solution Approach 2:
The patent dynamically adjusts the distillation loss amplitude using an adaptive parameter that is inversely related to the teacher model's gradient norm. This parameter change mechanism allows the student model to effectively learn from the teacher while maintaining training stability, resolving the contradiction between precision and speed.
2Measurement precision
If distillation loss amplitude is increased to improve student model precision, then precision is improved, but training stability deteriorates due to gradient conflict
Solution Approach 1:
The patent makes the distillation loss amplitude dynamic by introducing an adaptive parameter that adjusts during training based on the teacher model's gradient norm. This dynamic adjustment allows the system to maintain high precision while avoiding gradient conflict, as the loss amplitude automatically adapts to the training state.
Solution Approach 2:
The patent implements feedback by using the teacher model's gradient information to adjust the distillation loss amplitude. The adaptive parameter is calculated based on the gradient norm, creating a feedback loop that stabilizes training by reducing loss amplitude when gradient conflict is detected and increasing it when the teacher's guidance is reliable.
Data Source
AI summary
A model training method, a medium and an electronic device are provided. The method includes: inputting a training sample carrying a ground-truth label into a student model and a teacher model, and obtaining the first prediction result output by the student model and the second prediction result output by the teacher model; determining the first loss between the first prediction result and the ground-truth label, and determining the second loss between the first prediction result and the second prediction result; determining a total loss function according to the first loss, the second loss and an adaptive parameter; and adjusting a parameter of the student model based on the total loss function to obtain a trained student model.