Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices

Future research focuses on adapting lightweight models for specific domains, hybrid training, advanced data augmentation, explainability, and generational comparisons. Goals aim to enhance performance, efficiency, and adaptability across applications, optimizing for diverse datasets, emerging hardware, and energy efficiency. This improves model usability in resource-constrained environments, facilitating broader machine learning adoption. Deep learning optimizes neural networks for efficiency, balancing complexity and accuracy, with techniques like learning weights, connections, data augmentation, and hardware-aware design for edge computing. Applications include lightweight object detection, 6G technology, image super-resolution, pneumonia detection, and model prediction interpretation.