Performance Comparison of CNN Models Using Gradient Flow Analysis

Performance Comparison of CNN Models Using Gradient Flow Analysis

Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the locality and correlation of input data using structures i...

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Main Author: Seol-Hyun Noh
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Informatics
Subjects:
CNN
gradient vanishing problem
gradient flow
performance comparison
error rate
Online Access:https://www.mdpi.com/2227-9709/8/3/53
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spelling doaj-394aa58bcf8b424a92d40bf36b9c748a2021-09-26T00:26:18ZengMDPI AGInformatics2227-97092021-08-018535310.3390/informatics8030053Performance Comparison of CNN Models Using Gradient Flow AnalysisSeol-Hyun Noh0Department of Statistical Data Science, ICT Convergence Engineering, Anyang University, Anyang 14028, KoreaConvolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the locality and correlation of input data using structures in which convolutional layers are successively applied to the input data. In general, the performance of neural networks has improved as the depth of CNNs has increased. However, an increase in the depth of a CNN is not always accompanied by an increase in the accuracy of the neural network. This is because the gradient vanishing problem may arise, causing the weights of the weighted layers to fail to converge. Accordingly, the gradient flows of the VGGNet, ResNet, SENet, and DenseNet models were analyzed and compared in this study, and the reasons for the differences in the error rate performances of the models were derived.https://www.mdpi.com/2227-9709/8/3/53CNNgradient vanishing problemgradient flowperformance comparisonerror rate
collection DOAJ
language English
format Article
sources DOAJ
author Seol-Hyun Noh
spellingShingle Seol-Hyun Noh
Performance Comparison of CNN Models Using Gradient Flow Analysis
Informatics
CNN
gradient vanishing problem
gradient flow
performance comparison
error rate
author_facet Seol-Hyun Noh
author_sort Seol-Hyun Noh
title Performance Comparison of CNN Models Using Gradient Flow Analysis
title_short Performance Comparison of CNN Models Using Gradient Flow Analysis
title_full Performance Comparison of CNN Models Using Gradient Flow Analysis
title_fullStr Performance Comparison of CNN Models Using Gradient Flow Analysis
title_full_unstemmed Performance Comparison of CNN Models Using Gradient Flow Analysis
title_sort performance comparison of cnn models using gradient flow analysis
publisher MDPI AG
series Informatics
issn 2227-9709
publishDate 2021-08-01
description Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the locality and correlation of input data using structures in which convolutional layers are successively applied to the input data. In general, the performance of neural networks has improved as the depth of CNNs has increased. However, an increase in the depth of a CNN is not always accompanied by an increase in the accuracy of the neural network. This is because the gradient vanishing problem may arise, causing the weights of the weighted layers to fail to converge. Accordingly, the gradient flows of the VGGNet, ResNet, SENet, and DenseNet models were analyzed and compared in this study, and the reasons for the differences in the error rate performances of the models were derived.
topic CNN
gradient vanishing problem
gradient flow
performance comparison
error rate
url https://www.mdpi.com/2227-9709/8/3/53
work_keys_str_mv AT seolhyunnoh performancecomparisonofcnnmodelsusinggradientflowanalysis
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