Over the past few years, larger and deeper neural network models, particularly convolutional
neural networks (CNNs), have consistently advanced state-of-the-art performance across …

In this paper, we present CORING, which is short for effiCient tensOr decomposition-based
filteR prunING, a novel filter pruning methodology for neural networks. CORING is crafted to …

Most existing neural network pruning methods hand-crafted their importance criteria and
structures to prune. This constructs heavy and unintended dependencies on heuristics and …

Abstract Convolutional Neural Networks (CNNs) are frequently employed for image pattern
recognition and other computer vision tasks. When over-parameterized deep learning …

Deep convolutional neural networks (CNNs) have exhibited exceptional performance in a
range of computer vision tasks. However, these deep CNNs typically demand significant …

Deploying deep convolutional neural networks (DCNNs) on devices with low memory
resources or in applications with strict latency requirements remains a challenge. The weight …

Filter pruning can effectively reduce the time cost and computing resources of convolutional
neural networks (CNNs), and is well applied to lightweight edge devices. However, most of …

To address the problem of large parameters in the ResNet model, this article proposes a
rolling bearing fault diagnosis method with gram angle field (GAF) and Ghost-ResNet. For …

The enormous data and computational resources required by Convolutional Neural
Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive …

Convolutional neural networks (CNNs) play a crucial role and achieve top results in
computer vision tasks but at the cost of high computational cost and storage complexity. One …