Deep Convolutional Neural Network

Deep convolutional neural networks (CNNs) are a class of artificial neural networks designed to process data with a grid-like topology, such as images and videos, excelling at tasks like image classification, object detection, and segmentation. Current research focuses on improving CNN architectures (e.g., exploring variations of ResNet, Inception, and efficientNet models), developing novel training techniques (like integer-only training and self-knowledge distillation), and addressing challenges such as imbalanced datasets and catastrophic forgetting in incremental learning. The widespread application of CNNs across diverse fields, from medical image analysis and autonomous driving to agricultural monitoring and remote sensing, highlights their significant impact on both scientific understanding and practical problem-solving.