Structured Pruning for Deep CNN Image Classification
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Solution Overview
Problem
Deep Convolutional Neural Networks (CNNs) require significant computational and storage resources for image classification, making them inefficient and hindering their applicability, especially when using large-scale networks with large datasets.
Innovation Solution
An efficient image classification method based on structured pruning, which removes unimportant parameters by grouping weights, assigning regularization factors, and iteratively pruning weights to achieve a target sparsity rate, resulting in a sparsified network model that reduces computational and storage consumption while maintaining classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep CNN-based image classification algorithms are used to improve classification performance, then classification accuracy is improved, but computational and storage resources are excessively consumed
Solution Approach 1:
The patent applies parameter changes by modifying the network structure parameters through structured pruning. It changes the sparsity parameters of convolutional layers from 0 to non-zero values, transforming the dense network into a sparse network. This parameter transformation reduces the effective number of parameters while maintaining the network's representational capacity, thereby lowering computational and storage resource consumption while preserving classification accuracy.
Solution Approach 2:
The patent implements local quality by applying different sparsity levels to different layers and channels of the neural network. Instead of uniformly pruning all parameters, it identifies and prunes specific parameters locally based on their importance metrics (such as gradient magnitude or activation strength). This localized pruning approach maintains critical features in important regions while removing redundant parameters in less critical regions, achieving resource efficiency without sacrificing accuracy.
2Measurement precision
If large-scale networks are used to improve image classification performance, then classification accuracy is improved, but memory consumption increases
Solution Approach 1:
The patent transforms the parameter density of the network from dense to sparse by introducing sparsity parameters. This parameter change reduces the actual number of stored weights and activations, directly decreasing memory consumption. The sparse structure allows the network to maintain its functional capacity with fewer parameters, enabling large-scale networks to run with reduced memory footprint.
Solution Approach 2:
The patent extracts and removes redundant parameters from the large-scale network through structured pruning. By identifying and extracting unnecessary weights and connections based on importance metrics, it reduces the total parameter count that needs to be stored in memory. This extraction process maintains the essential features required for high accuracy while eliminating memory-intensive redundant elements.
3Productivity
If conventional image classification methods are used to reduce resource consumption, then computational efficiency is improved, but classification performance deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the network from dense to sparse structure, which improves computational efficiency through fewer operations. The sparsity parameters enable faster matrix multiplications and reduced activation computations, enhancing productivity. Simultaneously, the structured pruning maintains sufficient parameters to preserve classification accuracy, bridging the gap between efficiency and performance.
Solution Approach 2:
The patent segments the network parameters into important and redundant groups through structured pruning. By dividing parameters into retainable and removable sets based on importance metrics, it enables selective computation where only essential parameters are fully processed. This segmentation allows the network to achieve computational efficiency similar to conventional methods while maintaining the deep feature extraction capabilities needed for high accuracy.
Data Source
AI summary
The present invention provides an efficient image classification method based on structured pruning, which incorporates a spatial pruning method based on variation regularization, including steps such as image data preprocessing, inputting images to neural network, image model pruning and retraining, and new image class predication and classification. The present invention adopts a structured pruning method that removes unimportant weight parameters of the original network model and reduces unnecessary computational and memory consumptions caused by the network model in image classification to simplify the image classifier, and then uses the sparsified network model to predict and classify new images. The simplified method according to the present invention improves the original network model in image classification efficiency by nearly two times, costs about 30% less memory consumption and produces a better classification result.