Dense Network

Dense networks, characterized by extensive connections between layers or nodes, are a central theme in various machine learning applications, aiming to improve model performance and efficiency. Current research focuses on optimizing dense network architectures, such as incorporating dense convolutional layers, attention mechanisms, and efficient feature extraction modules, alongside exploring sparse subnetworks within dense models to reduce computational costs while maintaining accuracy. These advancements are impacting diverse fields, from image processing and speech recognition to graph analysis and federated learning, by enabling more accurate and efficient solutions for complex tasks.