Dynamic Integration Ratio for Neural Network Shared Layers
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Solution Overview
Problem
Existing neural network techniques face challenges in efficiently learning shared layers across multiple tasks, especially when task compatibility is unknown, leading to inefficient resource usage and performance trade-offs, particularly in hardware-constrained devices like cameras and smartphones.
Innovation Solution
An information processing apparatus and method that acquires an integration ratio for replacement layers in a hierarchical neural network, allowing for better learning of shared layers by dynamically determining the integration ratio based on learned parameters of replacement layers, enabling efficient learning even when task compatibility is unknown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If shared layers are used in a hierarchical neural network to learn multiple tasks, then network size is reduced and learning speed is improved, but task compatibility decreases and performance trade-offs occur
Solution Approach 1:
The patent applies dynamics by making the integration ratio dynamic rather than fixed. The integration ratio is determined based on the learned parameters of replacement layers, allowing the system to adaptively adjust the degree of layer sharing according to task compatibility. This resolves the contradiction by enabling the network to dynamically optimize between sharing benefits (learning speed) and task-specific performance (compatibility).
Solution Approach 2:
The patent changes the parameter of layer integration by introducing an integration ratio that varies based on learned parameters. Instead of using a fixed shared layer structure, the system adjusts the integration ratio parameter to reflect task compatibility, thereby resolving the performance trade-off while maintaining the efficiency benefits of layer sharing.
2Use of energy by moving object
If the size of the neural network is reduced for high-speed processing and low power consumption, then resource usage is improved, but learning efficiency decreases when task compatibility is unknown
Solution Approach 1:
The patent applies self-service by enabling the neural network to automatically determine the integration ratio based on its own learned parameters from replacement layers. This eliminates the need for external trial-and-error adjustment, allowing the system to self-optimize the balance between network size and learning efficiency without consuming additional resources on manual tuning.
Solution Approach 2:
The system uses feedback from the learned parameters of replacement layers to determine the integration ratio. This feedback mechanism allows the network to automatically adjust the degree of layer sharing based on actual task compatibility observed during learning, thereby maintaining learning efficiency while keeping the network size reduced for low power consumption.
3Reliability
If trial-and-error repetition of learning and adjustment is performed to determine task priorities and weights, then task compatibility is improved, but learning time increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the integration ratio determination method based on learned parameters of replacement layers. Instead of performing trial-and-error adjustments during the learning process, the system has a predetermined mechanism in place that directly determines the integration ratio, thereby eliminating the time-consuming trial-and-error phase while still achieving optimal task compatibility.
Data Source
AI summary
An information processing apparatus comprises one or more memories storing instructions and one or more processors that execute the instructions to acquire, by a process for learning a plurality of tasks, an integration ratio of output of replacement layers for which a shared layer, which is shared by a plurality of tasks in a hierarchical neural network, is replaced by a neural network layer for each task, and acquire a learned parameter of the shared layer based on the acquired integration ratio and learned parameters of the replacement layers acquired by the process for learning.