Homophily-Based Weight Combination for Cross-Domain Transfer Learning
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Solution Overview
Problem
Existing machine learning technologies face challenges in effectively transferring knowledge between domains with different feature spaces, leading to inefficiencies in cross-domain applicability and increased resource requirements for training multiple models.
Innovation Solution
The method involves determining the similarity between deep learning networks using a homophily value to combine weight vectors from source and target networks, facilitating the transfer of knowledge across domains by identifying transferrable layers and adjusting weights based on a homophily threshold condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge is transferred between domains with different feature spaces using conventional transfer learning, then the applicability of AI systems across domains is improved, but the resource requirements and training complexity increase significantly
Solution Approach 1:
The patent replaces conventional transfer learning mechanisms (which require complex domain adaptation, feature alignment, and extensive retraining) with a homophily-based weight combination mechanism. By quantifying homophily values between source and target network layers and automatically combining weights based on these values, the system eliminates the need for complex domain adaptation procedures while maintaining cross-domain applicability.
Solution Approach 2:
The patent introduces homophily values as new parameters that quantify the similarity between network layers across different domains. By changing the approach from direct feature space mapping to homophily-based weight combination, the system simplifies the transfer learning process while improving cross-domain adaptability.
2Reliability
If multiple deep learning networks are trained separately for different domains, then the accuracy for domain-specific tasks is maintained, but the time and resources required for development increase
Solution Approach 1:
The patent merges the training processes across multiple domains by combining weight vectors from source and target networks based on homophily values. This allows the system to leverage pre-trained networks while maintaining domain-specific accuracy, eliminating the need to train separate networks from scratch for each domain.
Solution Approach 2:
The patent utilizes pre-trained source networks as a foundation and applies homophily-based weight combination to adapt them to target domains. This preliminary action of using pre-trained weights significantly reduces development time while maintaining accuracy through the homophily threshold condition that ensures domain-specific performance.
3Reliability
If extensive training data is used for each domain, then the accuracy and robustness of domain-specific models are improved, but the resource requirements and training time increase
Solution Approach 1:
The patent copies weight vectors from source domain networks to target domain networks based on homophily values, rather than training target networks from scratch with extensive domain-specific data. This copying mechanism, guided by homophily thresholds, maintains model accuracy while significantly reducing the need for extensive training data and computational resources.
4Adaptability or versatility
If conventional transfer learning methods are applied to networks with different feature spaces, then cross-domain knowledge transfer is achieved, but the precision of feature representation is compromised
Solution Approach 1:
The patent introduces homophily values as an intermediary mechanism that bridges source and target domains with different feature spaces. Instead of directly mapping features between domains (which loses precision), the system uses homophily-based weight combination as an intermediary step that preserves feature representation precision while enabling cross-domain transfer.
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a pair of deep learning networks. A number of transferrable layers are determined and a homophily value indicating a level of similarity between layers of the same depth from the pair of the deep learning networks is determined. Upon ascertaining that the homophily value shows that the respective weight vectors of a layer of the same depth are to be combined based on a homophily threshold condition, the respective weight vectors for the layer is combined.