The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Capsule networks (CapsNets) have been known difficult to develop a deeper architecture,
which is desirable for high performance in the deep learning era, due to the complex …

Convolutional neural networks (CNNs) have shown excellent performance in numerous
computer vision tasks. However, the high computational and memory demands in computer …

Filter pruning is advocated for accelerating deep neural networks without dedicated
hardware or libraries, while maintaining high prediction accuracy. Several works have cast …

Neural architecture search (NAS) and network pruning are widely studied efficient AI
techniques, but not yet perfect. NAS performs exhaustive candidate architecture search …

Channel pruning has been widely studied as a prevailing method that effectively reduces
both computational cost and memory footprint of the original network while keeping a …

With the ever-increasing popularity of edge devices, it is necessary to implement real-time
segmentation on the edge for autonomous driving and many other applications. Vision …

Deep convolutional neural networks are shown to be overkill with high parametric and
computational redundancy in many application scenarios, and an increasing number of …

Abstract Dynamic Neural Networks (DNNs) are an evolving research field within deep
learning (DL), offering a robust, adaptable, and efficient alternative to the conventional Static …

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of
tasks, and deep learning holds the potential to advance a multitude of applications …