Pruning filters for efficient convolutional neural networks for image recognition in surveillance applications

Inactive Publication Date: 2018-11-22
NEC CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The patent describes a method and computer program for using a pruned collection of filters to recognize and predict conditions in an environment being surveilled. By extracting specific layers from a trained Convolutional Neural Network (CNN) and removing filters that are not important, the network is made more efficient and accurate. This allows for faster and more accurate image recognition for surveillance purposes. The technical effect is a more efficient and accurate surveillance system using a pruned cultural neural network.

Problems solved by technology

However, this trend also results in a greater need of the CNN for computational and power resources.
Thus, image recognition with CNNs is impractical, and indeed, in some instances, impossible in embedded and mobile application.
Simply compressing or pruning the weights of layers of a neural network would not adequately reduce the costs of a deep neural network.

Method used

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  • Pruning filters for efficient convolutional neural networks for image recognition in surveillance applications
  • Pruning filters for efficient convolutional neural networks for image recognition in surveillance applications
  • Pruning filters for efficient convolutional neural networks for image recognition in surveillance applications

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Experimental program
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Embodiment Construction

[0018]In accordance with the present principles, systems and methods are provided for a convolutional neural network (CNN) trained with pruned filters for image recognition in surveillance applications.

[0019]In one embodiment, the number of filters in a CNN is reduced by pruning. This pruning is accomplished by training a CNN for image recognition in a surveillance application. Once trained, the filters of the CNN can be assessed by determining the weights of each filter. By removing the filters that have small weights, the filters that have little contribution to accuracy can be removed, and thus pruned.

[0020]Once the filters have been pruned, the CNN can be retrained until it reaches its original level of accuracy. Thus, fewer filters are employed in a CNN that is equally accurate. By removing filters, the number of convolution operations and reduced, thus reducing computation costs, including computer resource requirements as well as power requirements. This pruning process also ...

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Abstract

Systems and methods for pruning a convolutional neural network (CNN) for surveillance with image recognition are described, including extracting convolutional layers from a trained CNN, each convolutional layer including a kernel matrix having at least one filter formed in a corresponding output channel of the kernel matrix, and a feature map set having a feature map corresponding to each filter. An absolute kernel weight is determined for each kernel and summed across each filter to determine a magnitude of each filter. The magnitude of each filter is compared with a threshold and removed if it is below the threshold. A feature map corresponding to each of the removed filters is removed to prune the CNN of filters. The CNN is retrained to generate a pruned CNN having fewer convolutional layers to efficiently recognize and predict conditions in an environment being surveilled.

Description

RELATED APPLICATION INFORMATION[0001]This application claims priority to 62 / 506,657, filed on May 16, 2017, incorporated herein by reference in its entirety. This application is related to an application entitled “PRUNING FILTERS FOR EFFICIENT CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE RECOGNITION OF ENVIRONMENTAL HAZARDS”, having attorney docket number 16085B, and an application entitled “PRUNING FILTERS FOR EFFICIENT CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE RECOGNITION IN VEHICLES”, having attorney docket number 16085C, and which are incorporated by reference herein in their entirety.BACKGROUNDTechnical Field[0002]The present invention relates to image recognition with neural networks and more particularly image recognition filter pruning for efficient convolutional neural networks for surveillance applications.Description of the Related Art[0003]Convolutional neural networks (CNNs) can be used to provide image recognition. As image recognition efforts have become more sophisticated, ...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N5/04G06K9/00G06V10/764G06V20/00G06V20/13G06V20/17
CPCG06N3/082G06N5/046G06K9/00771G06V20/00G06V20/13G06V20/58G06V20/52G06V10/454G06V20/17G06V10/82G06V10/764G06N3/044G06N3/045G06F18/2413G06N3/04G06V20/588G06F18/21G06F18/25
InventorKADAV, ASIMDURDANOVIC, IGORGRAF, HANS PETER
OwnerNEC CORP