Classical Convolutional Neural Network

Classical convolutional neural networks (CNNs) are a fundamental architecture in deep learning, designed to extract hierarchical features from data like images and signals through convolutional operations. Current research focuses on improving CNN efficiency and interpretability, exploring variations like depthwise-separable CNNs and hybrid models combining CNNs with transformers or recurrent networks to capture both local and global features. These advancements are driving improvements in diverse applications, including image classification, object detection, signal processing, and even financial modeling, by enhancing accuracy, reducing computational costs, and increasing model explainability.