Parallel Teacher-Student Neural Network Training
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Solution Overview
Problem
In guided learning methods, the need to pre-train a teacher neural network limits the ability of a student neural network to fully learn from it, resulting in suboptimal performance and prolonged training times, as the student neural network cannot effectively imitate and learn from the teacher during the teacher's training process.
Innovation Solution
Training the teacher and student neural networks in parallel allows the student to learn from the real-time updates of the teacher, using outputs from both networks to adjust parameters simultaneously, thereby enabling the student to understand the teacher's training process more comprehensively and reducing overall training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the teacher neural network is trained in advance before training the student neural network, then the teacher network can provide stable guidance features, but the overall training time is prolonged and the student network cannot learn from the teacher's training process
Solution Approach 1:
The patent applies preliminary action by initializing the student network with pre-trained weights from the teacher network before joint training begins. This allows the student network to start with a head start, having already absorbed some knowledge from the teacher's training process, thereby reducing the time needed to achieve comparable performance while still benefiting from the teacher's guidance throughout training.
Solution Approach 2:
The patent implements continuity of useful action by maintaining the teacher-student training relationship throughout the entire training process rather than separating them into distinct phases. The teacher network continues to provide guidance features during joint training, ensuring the student network continuously learns from the teacher's evolving knowledge, which optimizes learning efficiency and reduces total training time.
2Measurement precision
If the teacher neural network is trained in advance, then the training process can be structured sequentially, but the student network cannot effectively imitate and learn from the teacher's training process
Solution Approach 1:
The patent merges the teacher network training and student network training into a single joint training process. Both networks are trained simultaneously with shared loss functions that encourage the student to match the teacher's features while maintaining independent optimization. This combining approach allows the student to learn from the teacher's training process in real-time, achieving better feature distribution similarity without requiring complex multi-stage training protocols.
3Productivity
If parallel training of teacher and student networks is implemented, then training time is reduced, but coordination between the two networks becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms where the student network's loss is computed based on the teacher network's output features, and both networks receive gradient updates in a coordinated manner. The teacher network's features serve as soft targets for the student, creating a feedback loop that guides the student's learning while maintaining training efficiency. This structured feedback approach manages coordination complexity through well-defined loss functions and update rules.
Data Source
AI summary
Provided are a method, an apparatus and a system for training a neural network, and a storage medium storing instructions. The neural network comprises a first neural network and a second neural network, training of the first neural network has not yet completed and training of the second neural network does not start. The method comprises: obtaining a first output by subjecting a sample image to the current first neural network, and obtaining a second output by subjecting the sample image to the current second neural network; and updating the current first neural network according to a first loss function value, and updating the current second neural network according to a second loss function value. The performance of the second neural network can be improved, and the overall training time of the first neural network and the second neural network can be reduced.