Deep Learning Model Attention Feature Weighting
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Solution Overview
Problem
Deep learning models for image recognition face limitations in performance and precision due to incorrect information learning and lack of universality, particularly in feature extraction and processing, where features are not accurately related to real values and exhibit identical processing for training samples.
Innovation Solution
A deep learning model incorporating a determination layer to identify features at positions of attention within feature maps, with different weights assigned for attention and other features, enabling focused processing and improved recognition results through a compositing layer and fully-connected layer configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning model performs identical processing on all training samples, then training process is simple, but model performance and precision are limited
Solution Approach 1:
The patent applies local quality by differentiating the processing weights for different spatial regions in feature maps. Specifically, features corresponding to positions where attention objects are located are assigned different weights compared to other features. This allows the model to perform targeted processing on relevant regions while maintaining overall training efficiency, thereby improving recognition precision without sacrificing productivity.
2Device complexity
If deep learning model extracts features without position awareness, then feature extraction is simple, but extracted features are not related to real values
Solution Approach 1:
The patent introduces position awareness into feature extraction by assigning different weights to features based on their spatial positions in the feature maps. The determination layer identifies positions where attention objects are located, and the compositing layer applies corresponding weights to features at these positions. This preserves position information and ensures extracted features are related to real values without significantly increasing extraction complexity.
Solution Approach 2:
The training apparatus calculates attention loss based on the output of the determination layer and uses back propagation to adjust model parameters. This feedback mechanism ensures that position information is properly integrated into the feature extraction process, maintaining the relationship between extracted features and real values while managing complexity through iterative optimization.
3Adaptability or versatility
If deep learning model does not focus on attention features, then processing is uniform, but model lacks universality and learns incorrect information
Solution Approach 1:
The patent enhances model universality by enabling it to focus on attention features through differentiated weight processing. The determination layer identifies which features correspond to attention objects, and the compositing layer applies appropriate weights to these features. This allows the model to adapt to different scenarios and learn correct information patterns without requiring overly complex processing mechanisms.
Solution Approach 2:
The patent changes processing parameters dynamically based on feature importance. By adjusting weights according to whether features correspond to attention object positions or not, the model can adapt to various training scenarios and improve its universality. The training process optimizes these weight parameters through back propagation, balancing processing complexity with model adaptability.
Data Source
AI summary
Embodiments of this disclosure provide a deep learning model used for image recognition and apparatus and method thereof. The model includes a determination layer configured to determine whether features in feature maps are features of positions where objects of attention are located, and different weights are granted for the positions where the objects of attention are located and other features in performing weight and composition processing on the features. Hence, the model may be guided to be focused on attention features and make correct determination, thereby improving performance and precision of the model.