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Going More Deeper with Convolutions for Network in Network

by Neji Kouka, Jawaher Ben Khalfa, Jalel Eddine Hajlaoui
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 56
Year of Publication: 2024
Authors: Neji Kouka, Jawaher Ben Khalfa, Jalel Eddine Hajlaoui
10.5120/ijca2024924286

Neji Kouka, Jawaher Ben Khalfa, Jalel Eddine Hajlaoui . Going More Deeper with Convolutions for Network in Network. International Journal of Computer Applications. 186, 56 ( Dec 2024), 35-38. DOI=10.5120/ijca2024924286

@article{ 10.5120/ijca2024924286,
author = { Neji Kouka, Jawaher Ben Khalfa, Jalel Eddine Hajlaoui },
title = { Going More Deeper with Convolutions for Network in Network },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 56 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 35-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number56/going-more-deeper-with-convolutions-for-network-in-network/ },
doi = { 10.5120/ijca2024924286 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-27T02:46:01.256879+05:30
%A Neji Kouka
%A Jawaher Ben Khalfa
%A Jalel Eddine Hajlaoui
%T Going More Deeper with Convolutions for Network in Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 56
%P 35-38
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Network in Network is an important extension of the deep convolution neural network that uses a shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. In this article, we propose to replace convolution layers with convolution modules. The main feature of this architecture is the improved utilization of computing resources inside the network. This has been achieved through a carefully crafted design that allows for increased network depth and width while keeping the compute budget constant. The experimental results on the CIFAR10 dataset demonstrate the effectiveness of the proposed method.

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Index Terms

Computer Science
Information Sciences

Keywords

Convolutional Neural Networks (CNNs) Image recognition Network in Network (NiN)

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