Neural Network Feature Fusion for Complementary Attention Learning
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Solution Overview
Problem
Existing methods for training neural network models using multi-headed attention mechanisms fail to effectively model complex relationships between subspaces and extract complementary features due to insufficient feature fusion mechanisms, leading to inadequate representation capabilities.
Innovation Solution
A neural network model training method that involves nonlinear transformation of output vectors from multiple attention networks to generate a feature fusion vector, incorporating a complex relationship between these vectors, thereby enhancing the model's learning representation capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If linear splicing method is used to fuse output vectors of attention networks, then the fusion process is simple and fast, but the complex relationships between subspaces cannot be effectively modeled and representation capability is insufficient
Solution Approach 1:
The patent transforms the feature fusion process from linear splicing to nonlinear transformation by introducing learnable parameter matrices W and b. This parameter change enables the model to adaptively capture complex relationships between subspaces while maintaining computational efficiency through matrix operations.
Solution Approach 2:
The patent replaces the mechanical linear splicing operation with a neural network-based nonlinear transformation system. This substitution allows the model to learn complex patterns and relationships that cannot be captured by simple concatenation, thereby improving representation capability.
2Adaptability or versatility
If multiple attention networks are used to capture features from different subspaces, then the model can process diverse information, but the feature fusion mechanism fails to extract complementary features effectively
Solution Approach 1:
The patent merges the output vectors from multiple attention networks through nonlinear transformation instead of simple splicing. This merging process, governed by learnable parameters, enables effective extraction of complementary features while preserving the diverse information captured by each attention network.
Solution Approach 2:
The patent introduces an intermediary nonlinear transformation layer with learnable parameters that mediates between the multiple attention networks. This intermediary mechanism facilitates effective interaction and integration of features from different subspaces, preventing information loss.
3Device complexity
If simple feature fusion is used to reduce computational complexity, then the training process is faster and less resource-intensive, but the model learning representation capability is enhanced insufficiently
Solution Approach 1:
The patent applies local quality by introducing learnable parameters specifically at the feature fusion stage where they are most needed. This localized enhancement allows the model to maintain simplicity elsewhere while achieving superior representation capability through targeted nonlinear transformation.
Data Source
AI summary
Embodiments of this application disclose a neural network model training method, a machine translation method, a computer device, and a storage medium. The method includes: obtaining a training sample set including a training sample and a standard tag vector corresponding to the training sample; inputting the training sample into a neural network model including a plurality of attention networks to obtain a feature fusion vector; obtaining a predicted tag vector according to the feature fusion vector, and performing adjustment on a model parameter of the neural network model until a convergence condition is met to obtain a target neural network model.