Kernel Convolutional Neural Network

Kernel convolutional neural networks (CNNs) explore the use of larger convolutional kernels within CNN architectures to improve performance, particularly in image classification and segmentation tasks. Current research focuses on optimizing large kernel CNN designs, including exploring novel architectures like RepLKNet and incorporating techniques such as Mamba kernels and kernel segmentation to enhance efficiency and mitigate computational costs associated with larger kernels. These advancements aim to leverage the benefits of larger receptive fields for improved feature extraction and downstream task performance, impacting fields like medical image analysis and computer vision through more accurate and efficient models.