Neural Network Node Addition for Adaptive Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current neural networks require extensive and costly retraining when data changes, leading to decreased accuracy over time, making them impractical for continuous use, especially for smaller businesses without access to supercomputers.
Innovation Solution
A method and apparatus that dynamically add new nodes to the output layer of a neural network using autoencoders to reduce errors and adapt to changing data, allowing the network to maintain accuracy without full retraining, utilizing neurogenic deep learning and intrinsic replay data for stabilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full retraining of the neural network is performed when data changes, then accuracy is restored, but time and cost increase significantly
Solution Approach 1:
The patent segments the neural network into existing nodes and new nodes, allowing selective training of only the new nodes rather than retraining the entire network. This segmentation enables accuracy restoration while significantly reducing training time and computational resources by isolating the modification to specific network components.
Solution Approach 2:
The patent implements partial action by training only a subset of nodes (the new nodes) rather than performing complete retraining. This partial training approach restores accuracy to the extent needed by addressing only the nodes responsible for handling new data patterns, thereby reducing overall training time while maintaining sufficient accuracy.
2Measurement precision
If full retraining of the neural network is performed when data changes, then accuracy is restored, but computational cost increases significantly
Solution Approach 1:
The patent segments the neural network into existing nodes and new nodes, allowing selective training of only the new nodes rather than retraining the entire network. This segmentation enables accuracy restoration while significantly reducing training time and computational resources by isolating the modification to specific network components.
Solution Approach 2:
The patent implements partial action by training only a subset of nodes (the new nodes) rather than performing complete retraining. This partial training approach restores accuracy to the extent needed by addressing only the nodes responsible for handling new data patterns, thereby reducing overall training time while maintaining sufficient accuracy.
3Ease of manufacture
If the neural network structure remains fixed, then training is simpler, but adaptability to new data decreases
Solution Approach 1:
The patent implements dynamics by allowing the neural network structure to change over time through the addition of new nodes when drift is detected. The network transitions from a static structure to a dynamic one that can adapt its architecture, maintaining training simplicity for existing nodes while gaining adaptability to new data patterns through controlled structural modifications.
Solution Approach 2:
The system implements self-service by automatically detecting drift and adding new nodes without requiring complete retraining or manual intervention. The neural network serves itself by identifying when adaptation is needed and performing targeted modifications, thereby maintaining simplicity while improving adaptability to changing data conditions.
4Adaptability or versatility
If new nodes are added to the neural network, then adaptability to new data improves, but network complexity increases
Solution Approach 1:
The patent segments the neural network into existing nodes and new nodes, allowing selective training of only the new nodes rather than retraining the entire network. This segmentation enables accuracy restoration while significantly reducing training time and computational resources by isolating the modification to specific network components.
Solution Approach 2:
The patent implements feedback by continuously monitoring the neural network's performance and detecting drift between processed data and expected results. When drift exceeds a threshold, the system triggers the addition of new nodes, creating a feedback loop that manages complexity by only expanding the network when necessary, thereby balancing adaptability with controlled complexity.
5Measurement precision
If the neural network is trained on new data, then accuracy for new patterns improves, but training time increases
Solution Approach 1:
The patent segments the neural network into existing nodes and new nodes, allowing selective training of only the new nodes rather than retraining the entire network. This segmentation enables accuracy restoration while significantly reducing training time and computational resources by isolating the modification to specific network components.
Solution Approach 2:
The patent implements partial action by training only a subset of nodes (the new nodes) rather than performing complete retraining. This partial training approach restores accuracy to the extent needed by addressing only the nodes responsible for handling new data patterns, thereby reducing overall training time while maintaining sufficient accuracy.
Data Source
AI summary
A method and computer system for managing a neural network. Data is sent into an input layer in a portion of layers of nodes in the neural network. The data moves on an encode path through the portion such that an output layer in the portion outputs encoded data. The encoded data is sent into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer. A determination is made as to whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data. A number of new nodes is added to the output layer when a determination is present that the undesired amount of the error occurred, enabling reducing the error using the number of the new nodes.