Neural Network Incremental Growth for Continual Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning systems, particularly deep neural networks, face challenges in continual or lifelong learning, including forgetting previous tasks, slow learning rates, memory inefficiency, and vulnerability to adversarial attacks, while lacking robustness and generalization to new data.
Innovation Solution
The implementation of targeted incremental growth for neural networks through node-to-node relationship regularization, which involves adding nodes and arcs to the network to maintain performance, using one-shot learning, and employing node-to-node relationship regularization to guide the expanded network, enhance interpretability, and prevent forgetting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If nodes and arcs are added to the network to enable continual learning, then the ability to learn new tasks is improved, but the complexity of the network structure increases
Solution Approach 1:
The patent segments the network growth process into targeted incremental steps, adding nodes and arcs in a structured manner rather than uniformly expanding the entire network. This allows continual learning capability to be built progressively while managing complexity through controlled segmentation of the expansion process.
Solution Approach 2:
The patent introduces node-to-node relationship regularization as an additional dimensional constraint that guides network expansion. This regularization mechanism operates in the parameter space to control how new nodes and arcs are integrated, managing structural complexity while enabling continual learning.
2Adaptability or versatility
If the network is expanded to learn new tasks, then adaptability is improved, but memory requirements increase
Solution Approach 1:
The patent applies local quality by implementing node-to-node relationship regularization that operates locally between specific node pairs rather than globally across the entire network. This localized approach allows the network to adapt to new tasks while maintaining efficient memory usage through targeted rather than universal parameter adjustments.
3Productivity
If targeted incremental growth is implemented, then training efficiency is improved, but the difficulty of detecting and measuring network state increases
Solution Approach 1:
The patent incorporates feedback mechanisms through node-to-node relationship regularization that continuously monitors and adjusts the relationships between nodes during incremental growth. This feedback loop enables efficient targeted training while providing measurable indicators of network state changes, resolving the monitoring difficulty.
Data Source
AI summary
Computer systems and computer-implemented methods train a neural network, by:(a) computing for each datum in a set of training data, activation values for nodes in the neural network and estimates of partial derivatives of an objective function for the neural network for the nodes in the neural network; (b) selecting a target node of the neural network and/or a target datum in the set of training data; (c) selecting a target-specific improvement model for the neural network, wherein the target-specific improvement model, when added to the neural network, improves performance of the neural network for the target node and/or the target datum, as the case may be; (d) training the target-specific improvement model; (e) merging the target-specific improvement model with the neural network to form an expanded neural network; and (f) training the expanded neural network.