Channel-Wise Feature Data Recalibration in Convolution Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current feature data recalibration methods in convolution neural networks require significant computational resources and do not effectively differentiate the importance of each channel's feature data, leading to inefficient processing.
Innovation Solution
The proposed method performs independent affine transformations on feature data from each channel to obtain importance weights, allowing for channel-wise recalibration by multiplying or adjusting feature data based on these weights, which are learned during the neural network's training process, thereby enhancing or weakening feature data according to channel importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional feature data recalibration methods are used, then channel-wise feature enhancement can be achieved, but the computational resources required are excessive
Solution Approach 1:
The patent segments the feature data processing into independent channel-wise operations. Each channel's feature data is processed separately through its own affine transformation, avoiding the need for computationally intensive global processing while maintaining effective recalibration of important features
Solution Approach 2:
The patent applies different affine transformation parameters to different channels based on their individual importance. This local quality approach allows each channel to receive customized recalibration treatment, enhancing important features while consuming fewer computational resources compared to uniform processing methods
2Reliability
If feature data recalibration is performed to strengthen important channels, then recognition performance improves, but the processing complexity increases
Solution Approach 1:
The patent changes the parameters of affine transformations for each channel independently. By adjusting the transformation parameters based on channel importance, the method achieves effective feature recalibration that improves recognition performance without requiring complex processing architectures
3Productivity
If independent affine transformation is applied to each channel, then feature data recalibration efficiency improves, but the number of parameters increases
Solution Approach 1:
The patent divides the recalibration process into independent channel-wise affine transformations. This segmentation enables efficient parallel processing of each channel, improving overall recalibration efficiency while managing the number of parameters through localized rather than global processing
Data Source
AI summary
A method of recalibrating a feature data of each channel generated by a convolution layer of a convolution neural network is provided. According to some embodiments, since an affine transformation is applied to the feature data of each channel independently of the feature data of the other channel, the overall number of parameters defining the affine transformation is minimized. As a result, the amount of computations required in performing the feature data recalibration can be reduced.