Convolutional Neural Network Filter Structure Determination
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Solution Overview
Problem
The determination of a convolutional neural network structure for image recognition is complex and typically requires expertise, making it difficult for non-experts to design an effective structure for image recognition tasks.
Innovation Solution
A method that involves acquiring initial filters with trained weights, splitting them using transformations from image processing fields, and merging filters through clustering to automatically determine the structure of a convolutional neural network, allowing for the adjustment of filter numbers and improving identification performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design of convolutional neural network structure is performed by experts, then identification performance can be improved, but device complexity and difficulty of operation increase
Solution Approach 1:
The system performs automatic structure determination through self-organization of filters. Filters are automatically generated by applying transformations to initial filters and clustering them, eliminating the need for expert manual design while maintaining effective network structure for image recognition
Solution Approach 2:
The invention changes the parameter of filter number from fixed expert-determined value to dynamically adjustable value. By controlling the number of clustering centers, users can easily adjust the network structure parameters without requiring expertise in neural network design
2Ease of operation
If automatic structure determination is implemented, then ease of operation improves, but manufacturing precision may deteriorate
Solution Approach 1:
The system performs preliminary actions by generating multiple transformed filters from initial filters before clustering. This preliminary generation of diverse filter candidates ensures that the subsequent clustering process has high-quality inputs, maintaining determination accuracy while automating the process
Solution Approach 2:
The system uses clustering evaluation to provide feedback on the quality of filter grouping. By evaluating clustering results and iteratively adjusting the number of clustering centers, the system ensures accurate structure determination while maintaining ease of operation
3Reliability
If the number of filters is increased from N to M, then identification performance improves, but loss of time increases
Solution Approach 1:
The system performs preliminary training of initial filters before the splitting process. These pre-trained initial filters contain useful feature extraction capabilities that are then transformed and clustered, reducing the overall training time compared to training all filters from scratch
Solution Approach 2:
The system creates transformed copies of initial filters through various image processing transformations rather than training entirely new filters. This copying approach with transformations preserves effective filter patterns while generating diversity, reducing training time while maintaining performance
Data Source
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AI summary
A determination method for determining the structure of a convolutional neural network includes the acquisition step of acquiring N filters having the weights trained using a training image group as the initial values, where N is a natural number greater than or equal to 1, (S10) and the splitting step of increasing the number of the filters from N to M, where M is a natural number greater than or equal to 2 and is greater than N, by adding a filter obtained by performing a transformation used in image processing fields on at least one of the N filters (S20).