AI/ML Model Parameter Importance for Cross-Domain Knowledge Retention
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Solution Overview
Problem
Existing AI/ML models struggle to maintain performance across varying domains in wireless communications systems, leading to loss of knowledge when adapted for a new domain, rendering them ineffective in previous domains.
Innovation Solution
A method to adapt AI/ML models by identifying knowledge-intensive parameters and minimizing changes to these parameters during updates, while allowing adaptation to new domains, thus preserving knowledge from previous domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI/ML models are adapted for a new domain, then the model can perform in the new domain, but knowledge from previous domains is lost
Solution Approach 1:
The patent extracts and identifies knowledge-intensive parameters from the AI/ML model using gradient-based importance estimation. By separating these critical parameters from the full parameter set, the system can selectively update only non-critical parameters during domain adaptation, thereby preserving knowledge from previous domains while adapting to new domains.
Solution Approach 2:
The patent applies different update strategies to different parameters based on their importance. Knowledge-intensive parameters (identified through gradient analysis) are protected from updates or updated with stronger regularization, while non-knowledge-intensive parameters are freely updated. This local differentiation resolves the contradiction by allowing adaptation where needed while preserving knowledge where critical.
2Adaptability or versatility
If all model parameters are updated during adaptation, then the model adapts fully to the new domain, but performance in previous domains deteriorates
Solution Approach 1:
The patent changes the update behavior of model parameters based on their identified importance. By computing gradient-based importance metrics and using them to modulate parameter updates (through masking, regularization, or selective freezing), the system achieves partial parameter updates that balance adaptation to new domains with preservation of performance in previous domains.
3Loss of information
If knowledge-intensive parameters are identified and protected, then knowledge is preserved across domains, but the complexity of the adaptation process increases
Solution Approach 1:
The system performs self-assessment of parameter importance through gradient-based analysis during the adaptation process. By automatically identifying knowledge-intensive parameters through their gradient magnitudes and using this information to guide protection strategies, the system reduces the need for manual intervention or complex external assessment mechanisms, thereby managing complexity while preserving knowledge.
Data Source
AI summary
Various aspects of the present disclosure relate to adapting (or updating) an artificial intelligence/machine learning (AI/ML) model (e.g., a deep neural network) to unknown/new target domains while minimizing any loss of knowledge of previous domains within which the model was deployed or adapted. For example, an adaptation procedure may determine or identify knowledge intensive (e.g., important) parameters of the model and adapt the model to a new domain while minimizing changes to values of the knowledge intensive parameters. The procedure may determine a metric for each parameter of the model (e.g., a knowledge coefficient or importance metric) and minimize changes to values of any parameters having relatively high importance metrics during adaptation/updating of the model.