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 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.