Neural Network Weight Transfer for Personalized Model Updates
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Solution Overview
Problem
Existing neural network models require retraining when updated or transferred between devices, leading to inconvenience and inefficiency in personalization.
Innovation Solution
An electronic apparatus with communication and processing capabilities to compare, transfer, and update neural network models between devices, using weight information, training data, or the models themselves to ensure seamless personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a new version neural network model is received, then the performance of the neural network model is improved, but the personalized AI model training must be restarted causing inconvenience
Solution Approach 1:
The patent extracts the personalized weight parameters from the old model and applies them to the new model version. This separation allows the model structure to be updated while preserving the learned personalization characteristics, avoiding the need to retrain from scratch.
Solution Approach 2:
The system performs preliminary comparison between old and new model versions before deployment, identifying compatible parameters to transfer. This preliminary analysis ensures that personalization can be seamlessly applied to the new version without requiring complete retraining.
2Adaptability or versatility
If a new device is purchased, then the user gets new hardware capabilities, but the personal AI model experience cannot be transferred
Solution Approach 1:
The patent copies the personalized weight parameters from the old device's model to the new device's model. This copying mechanism enables transfer of personalization across devices without requiring the user to manually transfer or re-enter personalization data.
Solution Approach 2:
The system designs the model parameter structure to be universal across different device versions and types. By separating the personalized parameters from device-specific implementations, the same personalization can be applied across multiple devices and model versions.
3Adaptability or versatility
If the neural network model is retrained for personalization, then the model adapts to user preferences, but time and computational resources are consumed
Solution Approach 1:
Instead of retraining the entire model, the system only updates the local weight parameters that are responsible for personalization. This localized approach preserves the globally trained model structure while efficiently adapting to user preferences with minimal time and computational resources.
Data Source
AI summary
An electronic apparatus includes at least one memory configured to store at least one instruction and a first neural network model; a communicator comprising communication circuitry; and at least one processor configured to execute the at least one instruction to: receive, from an external electronic device, information on a second neural network model stored in the external electronic device through the communicator; compare the first neural network model with the second neural network model based on the information on the second neural network model; and control the communicator to transmit, to the external electronic device, information on a weight between nodes of the first neural network model based on comparison between the second neural network model and the first neural network model.