Convolution Layer

Convolutional layers are fundamental building blocks in convolutional neural networks (CNNs), designed to extract features from data by applying learned filters to input data. Current research focuses on improving efficiency and robustness of convolutional layers, exploring novel architectures like Columnar Stage Networks (CoSNet) for resource-constrained environments and analog optical implementations for faster processing. These advancements are significant for various applications, including image classification, object detection, medical image analysis, and signal processing, enabling more efficient and accurate models for diverse tasks.