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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Compressing Deep Networks by Neuron Agglomerative Clustering.

Li-Na Wang¹, Wenxue Liu¹, Xiang Liu^1,2

¹Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.

Sensors (Basel, Switzerland)

|October 29, 2020

Summary

This summary is machine-generated.

This study introduces Neuron Agglomerative Clustering (NAC) to compress deep neural networks (DNNs). NAC significantly reduces DNN size and computational demands, enhancing performance on limited resources without extra hardware.

Keywords:

agglomerative clustering deep learning feature maps network compression neurons

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Artificial Intelligence
Machine Learning
Computer Science

Background:

Deep neural networks (DNNs) show great success in various applications.
High-performance DNNs require substantial storage and computation, limiting their use in resource-constrained environments.

Purpose of the Study:

To introduce a novel method for compressing DNNs.
To reduce the storage space and computational time of DNNs without sacrificing accuracy.

Main Methods:

Neuron Agglomerative Clustering (NAC) algorithm is utilized to identify and agglomerate similar neurons and their connections.
The method compresses both fully connected and convolutional layers.

Main Results:

NAC significantly reduces the number of parameters and storage space of DNNs.
Experiments show NAC effectively compresses VGGNet by up to 92.96% on CIFAR-10 and 81.10% on CIFAR-100.
Compressed networks achieve similar or improved accuracy compared to original models.

Conclusions:

NAC is an effective technique for DNN compression, applicable to common network layers.
The method enhances DNN efficiency for deployment in resource-limited scenarios.