TripleNet: A Low Computing Power Platform of Low-Parameter Network

04/02/2022
by   Rui-Yang Ju, et al.
0

With the excellent performance of deep learning technology in the field of computer vision, convolutional neural network (CNN) architecture has become the main backbone of computer vision task technology. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. This paper proposes a lightweight convolutional neural network model, TripleNet, an improved convolutional neural network based on HarDNet and ThreshNet, inheriting the advantages of small memory usage and low power consumption of the mentioned two models. TripleNet uses three different convolutional layers combined into a new model architecture, which has less number of parameters than that of HarDNet and ThreshNet. CIFAR-10 and SVHN datasets were used for image classification by employing HarDNet, ThreshNet, and our proposed TripleNet for verification. Experimental results show that, compared with HarDNet, TripleNet's parameters are reduced by 66 ThreshNet, TripleNet's parameters are reduced by 37 increased by 5

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

03/06/2019

Compressing complex convolutional neural network based on an improved deep compression algorithm

Although convolutional neural network (CNN) has made great progress, lar...
09/29/2021

Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting

In this work, we propose Tiny-CRNN (Tiny Convolutional Recurrent Neural ...
12/19/2020

Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing

Large machine learning models based on Convolutional Neural Networks (CN...
10/30/2020

Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning

In this work, we present a robust and efficient solution for counting an...
10/16/2017

Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network

The success of deep convolutional neural network (CNN) in computer visio...
01/02/2020

Lightweight Residual Densely Connected Convolutional Neural Network

Extremely efficient convolutional neural network architectures are one o...
02/24/2018

Convolutional Neural Networks combined with Runge-Kutta Methods

A convolutional neural network for image classification can be construct...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.