Adaptable Neural Network System for Multi-Task Processing
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Solution Overview
Problem
Neural networks are typically adapted for specific tasks, requiring multiple networks for performing multiple assessments, which leads to high processing power and storage needs, and multi-task networks suffer from lack of personalization, resulting in reduced accuracy.
Innovation Solution
An adaptable neural network system comprising a first neural network and a second neural network, where the second network generates task-specific parameters to modify the first network, allowing it to perform various tasks without the need for multiple networks, thereby providing high personalization and dynamic flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural networks are created to perform multiple tasks, then task-specific accuracy is improved, but processing power and storage requirements increase significantly
Solution Approach 1:
The patent applies multi-functionality by designing a single neural network that can perform multiple tasks through dynamic reconfiguration. The network uses a shared base architecture with task-specific layers that can be selectively activated, allowing one network to replace multiple dedicated networks while maintaining task-specific accuracy and reducing overall computational resource consumption.
Solution Approach 2:
The patent implements dynamics by enabling the neural network to reconfigure its architecture dynamically based on the specific task at hand. Task-specific layers can be activated or deactivated, and network parameters can be adjusted in real-time, allowing the same network to adapt to different tasks without requiring multiple static networks, thus reducing processing power requirements.
2Measurement precision
If multiple neural networks are created to perform multiple tasks, then task-specific accuracy is improved, but storage requirements increase significantly
Solution Approach 1:
The patent applies multi-functionality by designing a single neural network that can perform multiple tasks through dynamic reconfiguration. The network uses a shared base architecture with task-specific layers that can be selectively activated, allowing one network to replace multiple dedicated networks while maintaining task-specific accuracy and reducing overall computational resource consumption.
Solution Approach 2:
The patent implements dynamics by enabling the neural network to reconfigure its architecture dynamically based on the specific task at hand. Task-specific layers can be activated or deactivated, and network parameters can be adjusted in real-time, allowing the same network to adapt to different tasks without requiring multiple static networks, thus reducing processing power requirements.
3Device complexity
If a single neural network is used for multiple tasks, then processing power and storage requirements are reduced, but personalization and accuracy for each task deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the neural network into a shared base architecture and separate task-specific layers. This modular segmentation allows the network to maintain a simple overall structure while incorporating specialized components for each task, enabling both efficiency and task-specific accuracy through selective activation of relevant segments.
Solution Approach 2:
The patent implements local quality by ensuring that different parts of the network have different levels of specialization. The shared base layers provide general functionality, while task-specific layers provide localized expertise for particular tasks. This allows the network to optimize for both general efficiency and specific task accuracy by activating only the necessary localized components.
Data Source
AI summary
An adaptable neural network system (1) formed of two neural networks (4, 5). One of the neural networks (5) adjusts a structure of the other neural network (4) based on information about a specific task each time that new second input data (12) indicative of a desired task is received by the one neural network (5), so that the other neural network (4) is adapted to perform that specific task. Thus, an adaptable neural network system (1) capable of performing different tasks on input data (11) can be realized.