Dense-Connection Knowledge Distillation for Lower-Cost DNN Training
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Solution Overview
Problem
Conventional knowledge distillation techniques for training deep neural networks (DNNs) require well-defined teacher networks, which may not be available in certain applications, and involve high training costs due to additional training stages and manual hyperparameter tuning, consuming significant computational resources and time.
Innovation Solution
A method where a teacher network is generated based on the structure of a student network, with internal and cross-network connections facilitating data transfer, allowing simultaneous training of both networks, reducing the need for predefined teacher networks and manual tuning, and enabling efficient conversion to lightweight DNNs with similar accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional knowledge distillation techniques are used to train deep neural networks, then accuracy can be maintained or improved, but training cost and computational resource consumption increase significantly
Solution Approach 1:
The patent merges the teacher network and student network into a single training framework where both networks share the same training process. The teacher network (larger model) and student network (smaller model) are trained simultaneously with shared parameters and loss functions, eliminating the need for separate training stages. This merging reduces computational overhead and training costs while maintaining the accuracy benefits of knowledge distillation.
Solution Approach 2:
The training framework is designed to be universal and adaptable to different network architectures and sizes. The same distillation loss function and training mechanism can be applied regardless of whether the teacher network is significantly larger than the student network or only moderately larger. This multi-functionality allows the system to work efficiently across various application scenarios without requiring architecture-specific optimizations.
2Productivity
If conventional knowledge distillation techniques are used, then a compact student network can be trained, but well-defined teacher networks must be available and manual hyperparameter tuning is required
Solution Approach 1:
The system performs self-service by automatically determining the relationship between teacher and student networks without requiring external definition. The teacher network is identified as the larger model and the student network as the smaller model within the same training framework. The method automatically adjusts to the available network configurations and performs knowledge distillation without needing pre-defined teacher-student pairs or external configuration files.
Solution Approach 2:
Conventional approaches define the teacher network first and then train a student network to mimic it. This patent inverts the approach by allowing either network to serve as teacher or student based on their relative sizes and capabilities. The larger network naturally becomes the teacher and the smaller network becomes the student through the distillation process, eliminating the need for explicit teacher network definition.
3Loss of information
If conventional knowledge distillation techniques are used, then knowledge transfer can occur, but multiple training stages and manual hyperparameter tuning consume significant time
Solution Approach 1:
The patent performs preliminary action by establishing the teacher-student relationship and configuring the distillation framework before training begins. The larger network is automatically identified as the teacher and the smaller network as the student, and the distillation loss function is pre-configured to work with both networks. This preliminary setup eliminates the need for multiple training stages and manual hyperparameter tuning during the training process, significantly reducing training time.
Solution Approach 2:
The training process maintains continuity by performing knowledge distillation and standard training simultaneously in a single unified framework. Both the teacher network and student network are trained continuously together, with the distillation loss and standard loss functions working in parallel. This continuous joint training eliminates idle time between training stages and ensures that knowledge transfer occurs throughout the entire training process.
Data Source
AI summary
A neural network can be trained through knowledge distillation. A support neural network is generated based on a target neural network. The support neural network is a teacher model, and the target neural network is a student model. The support neural network may have same layers as the target neural networks. Some or all layers of the support neural network may be connected to facilitate data transfer between these layers. The support neural network and target neural network are merged into a merged network. The merged network is trained. At least one layer in the support neural network is connected to a layer in the target neural network to facilitate data transfer from the target neural network to the support neural network during the training. After the training, the target neural network is separated from the merged network and can be used to perform machine learning tasks.