Lightweight ConvNets

Lightweight convolutional neural networks (ConvNets) aim to achieve high performance in image and signal processing tasks while minimizing computational cost and memory footprint. Current research focuses on developing efficient architectures, such as all-convolutional networks and those incorporating large kernel sizes, and on techniques like patch pruning to reduce redundancy without significant accuracy loss. These advancements are crucial for deploying deep learning models on resource-constrained devices and for improving the efficiency of various applications, including medical image segmentation, gesture recognition, and land cover classification. The resulting models offer a compelling balance between accuracy and efficiency, broadening the accessibility and applicability of deep learning.