Multi-Teacher Neural Network Learning Device for Autonomous Driving
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Solution Overview
Problem
The construction of high-quality labeled training datasets for deep neural networks in autonomous driving is costly and time-consuming, and semi-supervised learning with pseudo labeling is limited by the vulnerabilities of neural networks, leading to suboptimal performance in student neural networks trained using teacher neural networks.
Innovation Solution
A learning device and method that employs multiple teacher neural networks and an error identification network to generate and refine pseudo labels for student neural networks, improving performance by integrating prediction information and error identification predictions to update parameters effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single teacher neural network is used for knowledge distillation, then the training process is simple and fast, but the student neural network inherits the same vulnerabilities and achieves suboptimal performance
Solution Approach 1:
The patent combines multiple teacher neural networks with different architectures and strengths into a unified knowledge distillation system. Each teacher network contributes its unique advantages, and their predictions are aggregated to provide more comprehensive and accurate training signals for the student network, thereby improving reliability while managing complexity through structured integration.
Solution Approach 2:
The patent creates a composite teaching system by integrating multiple teacher neural networks with different architectural characteristics (e.g., CNN, RNN, Transformer). Each teacher network acts as a different 'material' with unique properties, and their combined output forms a composite knowledge source that is more robust and versatile than any single teacher network alone.
2Reliability
If multiple teacher neural networks are used for knowledge distillation, then the performance of student neural network is improved, but the system complexity and computational cost increase
Solution Approach 1:
The patent segments the knowledge distillation process into distinct components: multiple teacher networks generate predictions independently, an error identification network separately analyzes prediction accuracy, and a parameter update device systematically adjusts student network parameters. This segmentation allows each component to be optimized independently and simplifies the overall system management while maintaining high performance.
Solution Approach 2:
The patent introduces an error identification network as an intermediary component that mediates between multiple teacher networks and the student network. This intermediary analyzes the accuracy of teacher predictions and provides refined training signals, reducing the direct complexity burden on the student network while improving learning efficiency and performance.
3Measurement precision
If pseudo labeling is used with a single teacher neural network, then the training cost is reduced, but the accuracy of pseudo labels is limited by the teacher network's vulnerabilities
Solution Approach 1:
The patent merges predictions from multiple teacher neural networks to generate pseudo labels, combining their individual strengths to overcome shared vulnerabilities. This ensemble approach produces more accurate and reliable pseudo labels while maintaining the cost-effectiveness of semi-supervised learning, as the computational resources required are still far less than constructing fully labeled datasets.
4Reliability
If traditional knowledge distillation is used, then the training process is efficient, but the student neural network cannot overcome the vulnerabilities of the teacher network
Solution Approach 1:
The patent performs preliminary error identification and analysis before the main knowledge distillation training. The error identification network pre-assesses the accuracy of teacher predictions and identifies problematic cases in advance, allowing the student network to focus learning efforts on critical areas. This preliminary action reduces unnecessary training iterations and accelerates convergence while improving final performance.
Solution Approach 2:
The patent implements a feedback mechanism where the error identification network continuously monitors student network performance and adjusts training signals accordingly. This feedback loop enables dynamic optimization of the knowledge distillation process, correcting errors and refining predictions in real-time, which improves student network performance while maintaining training efficiency through targeted adjustments rather than exhaustive retraining.
Data Source
AI summary
Machine learning and testing devices are provided. The device applies a neural network operation of a first student neural network to a training image to generate first prediction information, applies a neural network operation of a second student neural network to generate second prediction information, applies an error identification operation to a first integrated image to generate first error identification prediction information, applies the error identification operation to a second integrated image to generate second error identification prediction information, applies a network operation of a first teacher neural network to the training image to generate first pseudo label information, applies a neural network operation of a second teacher neural network to the training image to generate second pseudo label information, back-propagates a loss and updates parameters of the first and second student neural networks.