Neural Network Knowledge Distillation for Model Miniaturization
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Solution Overview
Problem
Existing methods for training artificial neural networks face challenges in minimizing recognition rate reduction while miniaturizing the network, and in efficiently selecting teacher models for training student models.
Innovation Solution
A model training method that selects a teacher model from a plurality of teacher models based on accuracy and correlation, and iteratively trains a student model using output data from the selected teacher models, with different loss calculations and weight applications to optimize the student model's performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the neural network is miniaturized to reduce device complexity and resource usage, then the network size is reduced, but the recognition rate deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training a large-scale teacher model to achieve high recognition accuracy, then using this pre-trained model to generate training data for a smaller student model. This preliminary training of the teacher model enables the student model to inherit knowledge without requiring extensive training data, thus resolving the contradiction between model size and recognition rate.
Solution Approach 2:
The patent uses copying by creating a student model that replicates the knowledge and patterns learned by the teacher model. Through knowledge distillation, the student model copies the essential features and decision-making patterns from the larger teacher model, enabling the smaller model to achieve comparable recognition performance with reduced complexity.
2Reliability
If multiple teacher models are used to train the student model to improve recognition rate, then the training effectiveness is improved, but the device complexity and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively using only the necessary components from multiple teacher models. Instead of incorporating all features from all teacher models, the method selects and transfers only the most relevant knowledge and patterns that contribute to improving the student model's recognition rate, thus avoiding unnecessary complexity.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the number and configuration of teacher models based on the specific training requirements and available resources. The system can adaptively change parameters such as the number of teacher models, their architectures, and the knowledge distillation weights to optimize the balance between recognition rate improvement and device complexity.
Data Source
AI summary
Disclosed herein are a model training method, a data recognizing method, and a model training apparatus. A model training method includes selecting a teacher model from a plurality of teacher models; receiving, at a student model, input data; and training the student model based on output data of the selected teacher model, the output data corresponding to the input data.