Neural Network Feature Extraction via Gradient Filtering
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Solution Overview
Problem
Deep artificial neural networks suffer from 'catastrophic forgetting' during continual learning, leading to a decline in accuracy and precision in feature extraction, making them incapable of adapting to new information and tasks.
Innovation Solution
A feature extraction system utilizing neural network optimization by gradient filtering, which iteratively updates and optimizes feature extraction networks through gradient filtering, allowing for continuous learning and adaptation by modifying weights and filter parameters based on training task data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a feature extraction network constructed based on artificial neural network is used for continual learning, then the network can learn new information and adapt to new tasks, but catastrophic forgetting occurs causing a drastic decline in accuracy and precision of feature extraction
Solution Approach 1:
The patent segments the feature extraction network into multiple independent modules, each responsible for extracting features from specific domains or task types. This modular architecture allows the network to learn new tasks in isolated modules without interfering with previously learned modules, thereby preventing catastrophic forgetting while maintaining overall system reliability for feature extraction.
Solution Approach 2:
The patent implements dynamic architecture where the feature extraction network can adapt its structure and learning parameters in real-time based on the current task requirements. The system dynamically adjusts learning rates, activation functions, and network connectivity based on task complexity and data characteristics, enabling continual learning without degrading performance on previous tasks.
2Adaptability or versatility
If the feature extraction network is updated continuously to adapt to new tasks, then the network becomes more adaptable, but the stability of previously learned feature mappings is destroyed
Solution Approach 1:
The patent divides the feature extraction network into stable, frozen modules for previously learned tasks and dynamic, trainable modules for new tasks. This segmentation maintains the stability of established feature mappings while allowing flexible updates in new modules, resolving the contradiction between adaptability and stability.
Solution Approach 2:
The patent performs preliminary actions by pre-training and freezing specific network modules before introducing new tasks. This preliminary stabilization ensures that previously learned feature mappings remain intact while new modules are trained independently, preventing the destruction of stable mappings during continuous updates.
3Reliability
If sample saving or key sample extraction methods are used to prevent catastrophic forgetting, then the network can retain previous knowledge, but computational resources are consumed
Solution Approach 1:
The patent segments the learning process into independent module-level updates rather than requiring global retraining or sample saving. Each module can be updated independently based on its specific task requirements, eliminating the need for computationally intensive sample saving operations while maintaining knowledge retention through modular architecture.
Solution Approach 2:
The patent implements self-service learning where each network module automatically adjusts its own parameters based on task-specific data without requiring external intervention for sample management. The system self-regulates learning processes, eliminating the need for computationally resource-intensive key sample extraction or saving mechanisms.
Data Source
AI summary
A feature extraction system, method and apparatus based on neural network optimization by gradient filtering is provided. The feature extraction method includes: acquiring, by an information acquisition device, input information; constructing, by a feature extraction device, different feature extraction networks, performing iterative training on the networks in combination with corresponding training task queues to obtain optimized feature extraction networks for different input information, and calling a corresponding optimized feature extraction network to perform feature extraction according to a class of the input information; performing, by an online updating device, online updating of the networks; and outputting, by a feature output device, a feature of the input information. The new feature extraction system, method and apparatus avoids the problem of catastrophic forgetting of the artificial neural network in continuous tasks, and achieves high accuracy and precision in continuous feature extraction.