Examination of the Relationship between Feature Extraction by Kernels and CNN Performance

Convolutional Neural Networks (CNNs) excel at various image-related tasks. These networks extract local features from images using small-sized kernels. By stacking multiple convolutional layers, they can not only focus on local regions but also capture broader global features of the input image. In...

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Bibliographic Details
Published in2024 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 1 - 5
Main Authors Togawa, Sora, Jin'No, Kenya
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.05.2024
Subjects
Circuits and systems
CNN
Convolutional neural networks
Explainable AI
Feature extraction
Filter
Frequency Analysis
Image Recognition
Kernel
Stacking
Task analysis
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Summary:Convolutional Neural Networks (CNNs) excel at various image-related tasks. These networks extract local features from images using small-sized kernels. By stacking multiple convolutional layers, they can not only focus on local regions but also capture broader global features of the input image. In this article, we'll examine the kernels learned by various CNNs to understand the features they extract. We'll also assess the significance of these kernels and discuss how they relate to the CNN's performance.
ISSN:2158-1525
DOI:10.1109/ISCAS58744.2024.10557842