Deep Learning Module

Deep learning modules are specialized sub-networks within larger deep learning models, designed to perform specific tasks or enhance overall model performance. Current research focuses on improving module architectures, such as exploring alternatives to traditional tree-structured networks (e.g., using fully connected or yoked structures) and developing modules optimized for particular applications like image processing (e.g., using shifted-pillars concatenation for improved local feature extraction) or sensor data integration. These advancements aim to improve model efficiency, accuracy, and applicability across diverse domains, including robotics education, image compression for machine vision, and remote sensing.