Dynamic Integration Cycle for Multitask Distributed Learning
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Solution Overview
Problem
In multitask distributed learning, differences in recognition accuracy between tasks can arise due to varying task characteristics and data amounts, leading to impaired user experience and potential deterioration of shared layer weights into local solutions, resulting in limited further learning improvements.
Innovation Solution
An information processing apparatus that performs distributed learning by integrating weight parameters of shared layers across recognition models, with an adjustable integration cycle set based on evaluation of recognition accuracies to balance recognition tasks, allowing for longer individual learning periods to improve accuracy in tasks with lower performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If distributed learning is performed with frequent integration of shared layer weights, then convergence speed is improved, but recognition accuracy of individual tasks deteriorates due to loss of task-specific adaptations
Solution Approach 1:
The integration cycle is made dynamic and adaptive rather than fixed. The system automatically adjusts the integration cycle based on evaluation of recognition accuracy, extending the cycle when accuracy deteriorates and shortening it when accuracy is sufficient. This dynamic adjustment resolves the contradiction by allowing frequent integration when beneficial for convergence while preventing excessive integration that would harm task-specific performance.
Solution Approach 2:
The system implements feedback mechanisms where recognition accuracy is continuously evaluated and used to control the integration process. The evaluation result feeds back to adjust the integration cycle, creating a closed-loop control system that balances convergence speed and recognition accuracy by adapting the integration frequency to actual performance needs.
2Measurement precision
If integration cycle is extended to improve individual task learning, then recognition accuracy of individual tasks is improved, but overall learning efficiency deteriorates due to slower weight integration
Solution Approach 1:
The integration cycle dynamically adapts based on task performance evaluation. When individual task accuracy is sufficient, the cycle shortens to improve learning efficiency. When accuracy needs improvement, the cycle extends to allow more individual learning. This dynamic behavior resolves the contradiction by making the system productive when tasks are performing well while maintaining accuracy when they need improvement.
Solution Approach 2:
The system changes the integration cycle parameter based on evaluation results. By adjusting this key parameter dynamically, the system optimizes the balance between individual task learning (requiring longer cycles) and overall learning efficiency (benefiting from shorter cycles), resolving the contradiction through adaptive parameter modification.
3Productivity
If weight parameters are integrated frequently across recognition models, then shared layer learning is accelerated, but task-specific performance differences increase due to premature integration
Solution Approach 1:
The system uses feedback from task-specific recognition accuracy evaluation to control the integration process. When accuracy indicates that tasks are ready for integration, the system performs weight integration to accelerate shared layer learning. When accuracy shows tasks need more individual learning, integration is delayed. This feedback mechanism resolves the contradiction by timing integration optimally for both shared learning speed and task-specific accuracy.
Solution Approach 2:
The system performs preliminary individual task learning before integrating weight parameters. By allowing tasks to develop their specific characteristics first (preliminary action), the system ensures that subsequent integration of shared layers does not prematurely homogenize task-specific performances, thereby maintaining both learning speed and accuracy.
Data Source
AI summary
There is provided with an information processing apparatus. A performing unit performs, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning data. A reconstructing unit reconstructs, during the learning, the plurality of recognition models by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters. A setting unit sets, during the learning, an integration cycle for performing the integration.