Dynamic Conditional Pooling for Neural Network Feature Aggregation
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Solution Overview
Problem
Conventional pooling methods in neural networks fail to effectively capture the discriminative properties of individual image or video samples and their feature distributions, leading to reduced robustness and accuracy in visual recognition tasks.
Innovation Solution
Dynamic conditional pooling technology that uses learnable convolutional filters for adaptive aggregation of feature maps, followed by normalization and soft weight generation to conditionally aggregate and normalize features based on the input sample, allowing for sample-aware and distribution-adaptive processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pooling methods (average pooling, max pooling) are used to downsample feature maps, then the feature maps become more robust to position changes, but the discriminative properties of individual image samples and their feature distributions are lost
Solution Approach 1:
The patent applies dynamics by replacing static pooling operations with dynamic conditional pooling. The pooling operation is made adaptive through learnable convolutional filters that dynamically adjust based on the input sample and feature distribution. This allows the system to maintain robustness while preserving discriminative properties by conditioning the pooling behavior on the specific characteristics of each sample.
Solution Approach 2:
The patent changes the parameters of the pooling operation by introducing learnable convolutional filters with different kernel sizes and stride values. Instead of using fixed pooling parameters, the system learns optimal parameters adaptively, allowing different regions of the feature map to be pooled differently based on their importance and distribution characteristics.
2Measurement precision
If multiple feature detectors are used to address complex images, then the accuracy of visual recognition improves, but the processing load increases significantly
Solution Approach 1:
The patent applies universality by designing a single dynamic conditional pooling layer that performs multiple functions simultaneously. Instead of using separate feature detectors, the learnable convolutional filters can adapt to detect different features based on the input data, reducing the overall processing load while maintaining recognition accuracy.
Solution Approach 2:
The patent applies partial action by using learnable convolutional filters with varying kernel sizes that selectively process only the most important regions of the feature map. This allows the system to focus computational resources on discriminative regions rather than processing the entire feature map uniformly, reducing overall processing load.
3Reliability
If feature maps are downsampled to make them more robust, then the sensitivity to feature location decreases, but the detail information is lost
Solution Approach 1:
The patent applies local quality by implementing spatially varying pooling operations where different regions of the feature map are processed differently. Learnable convolutional filters with different parameters are applied to different regions based on their local characteristics, allowing important detailed regions to be preserved while less critical regions are more heavily pooled.
Data Source
AI summary
Dynamic conditional pooling for neural network processing is disclosed. An example of a storage medium includes instructions for receiving an input at a convolutional layer of a convolutional neural network (CNN); receiving an input sample at a pooling stage of the convolutional layer; generating a plurality of soft weights based on the input sample; performing conditional aggregation on the input sample utilizing the plurality of soft weights to generate an aggregated value; and performing conditional normalization on the aggregated value to generate an output for the convolutional layer.