Neural Network Training Parameter Optimization for Multi-Task Recognition
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Solution Overview
Problem
Current techniques lack an effective method for setting training parameters to improve identification accuracy when training a neural network for multiple recognition tasks simultaneously.
Innovation Solution
An information processing apparatus with a task setting unit, training unit, evaluation unit, and parameter setting unit is used to set and adjust training parameters based on evaluation results, optimizing the training of a multilayer neural network or classifier for multiple recognition tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training parameters are not optimized, then training process is simple, but identification accuracy for multiple recognition tasks is insufficient
Solution Approach 1:
The patent implements feedback mechanisms where training progress and performance metrics are continuously monitored and fed back to adjust training parameters. The system evaluates identification accuracy across multiple recognition tasks and uses this feedback to dynamically optimize learning rates, regularization parameters, and other training configurations, thereby resolving the contradiction between achieving high accuracy and maintaining simple training processes.
Solution Approach 2:
The patent systematically changes training parameters such as learning rates, batch sizes, and regularization strengths based on observed performance. By implementing parameter optimization techniques that adaptively adjust these parameters during training, the system improves identification accuracy while managing the complexity through structured parameter search and adjustment protocols.
2Measurement precision
If training parameters are optimized for high accuracy, then identification performance improves, but training time and computational resources increase
Solution Approach 1:
The patent applies partial optimization strategies where not all training parameters are optimized to maximum precision, but rather a subset of critical parameters is adjusted to achieve sufficient accuracy. This approach allows the system to obtain good enough performance without investing excessive computational resources and time in comprehensive parameter optimization.
Solution Approach 2:
The patent implements periodic parameter optimization where training parameters are adjusted at specific intervals rather than continuously. This periodic adjustment strategy reduces the overall computational burden and training time while still achieving improved identification accuracy through strategically timed parameter optimizations.
3Adaptability or versatility
If multiple recognition tasks are trained simultaneously, then system versatility improves, but training parameter optimization becomes more complex
Solution Approach 1:
The patent implements universal training parameter optimization strategies that work across multiple different recognition tasks. By developing parameter adjustment mechanisms that are task-agnostic or broadly applicable, the system can handle diverse recognition tasks (such as object detection, classification, and segmentation) with a unified optimization framework, thereby managing complexity while maintaining versatility.
Solution Approach 2:
The patent segments the optimization process for multiple tasks into independent or semi-independent sub-problems. By dividing the complex multi-task parameter optimization into task-specific modules or hierarchical levels, the system can optimize parameters for each task or group of tasks separately, reducing the overall complexity while maintaining the ability to handle multiple recognition tasks simultaneously.
Data Source
AI summary
An apparatus includes a multitask setting unit configured to set a plurality of recognition tasks for which a multilayer neural network is trained, a neural network (NN) training unit configured to train the multilayer NN for the set plurality of recognition tasks, an NN evaluation unit configured to evaluate a training result of the multilayer NN, and a training parameter setting unit configured to set a training parameter in training the multilayer NN for the plurality of recognition tasks, based on a result of evaluation by the NN evaluation unit.