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 …

Deep learning (DL) is progressively popular as a viable alternative to traditional signal
processing (SP) based methods for fault diagnosis. However, the lack of explainability …

Network pruning has attracted increasing attention recently for its capability of transferring
large-scale neural networks (eg, CNNs) into resource-constrained devices. Such a transfer …

Abstract Deep Neural Networks (DNNs) enjoy the welfare of convolution, while also bearing
huge computational pressure. Therefore, model compression techniques are used to …

Neural network pruning, an important method to reduce the computational complexity of
deep models, can be well applied to devices with limited resources. However, most current …

As the deep learning models have been widely used in recent years, there is a high demand
for reducing the model size in terms of memory and computation without much compromise …

Embedded systems are power sensitive and have limited memory hence inferencing large
networks on such systems is difficult. Pruning techniques have been instrumental in …

Structured pruning is an efficient compression technique that significantly reduces the
inference latency and energy consumption of convolutional neural networks (CNNs) by …

In the realm of practical fine-grained visual classification applications rooted in deep
learning a common scenario involves training a model using a pre-existing dataset …

With the development of modern military technology, electrical drive technology has become
a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece …