Convolution Kernel

Convolution kernels are the fundamental building blocks of convolutional neural networks (CNNs), responsible for extracting features from data by performing weighted sums of local input regions. Current research focuses on improving kernel design for efficiency and performance, exploring variations such as adaptive, rotated, and oversized kernels, often integrated within architectures like U-Net and various transformer-inspired CNNs. These advancements aim to enhance model accuracy, interpretability, and efficiency across diverse applications, including medical image segmentation, object detection, and graph classification, while addressing challenges like computational cost and generalization.