Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

The use of object detection algorithms has become extremely important in autonomous
vehicles. Object detection at high accuracy and a fast inference speed is essential for safe …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Previous works utilized" smaller-norm-less-important" criterion to prune filters with smaller
norm values in a convolutional neural network. In this paper, we analyze this norm-based …

Filter pruning has been widely applied to neural network compression and acceleration.
Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune …

Making deep convolutional neural networks more accurate typically comes at the cost of
increased computational and memory resources. In this paper, we reduce this cost by …

Deep neural networks offer considerable potential across a range of applications, from
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …

This paper presents a survey of methods for pruning deep neural networks. It begins by
categorising over 150 studies based on the underlying approach used and then focuses on …

We study how to set channel numbers in a neural network to achieve better accuracy under
constrained resources (eg, FLOPs, latency, memory footprint or model size). A simple and …