Convolutional Neural Network Architecture

Convolutional Neural Networks (CNNs) are a fundamental deep learning architecture designed for processing grid-like data, primarily images, by leveraging convolutional filters to extract features at multiple scales. Current research emphasizes improving CNN efficiency through techniques like early exiting for faster inference, optimized architectures such as EfficientNets and specialized designs for low-resource environments (e.g., mobile devices, embedded systems), and exploring novel approaches such as incorporating attention mechanisms and graph neural networks for specific applications. The widespread applicability of CNNs across diverse fields, from medical image analysis and object detection to materials science and signal processing, underscores their significant impact on both scientific understanding and practical technological advancements.