Lightweight Deep

Lightweight deep learning focuses on developing deep neural networks that are computationally efficient and require minimal resources, enabling deployment on devices with limited processing power like mobile phones and embedded systems. Current research emphasizes model compression techniques, novel architectures (e.g., MobileNet, ShuffleNet, binary neural networks), and knowledge distillation to improve accuracy while reducing model size and computational cost. This field is crucial for expanding the applications of deep learning to resource-constrained environments, impacting diverse areas such as medical imaging, real-time object detection, and mobile AI.