Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Due to their learning and adaptation capabilities, convolutional neural networks (CNNs)
have demonstrated potential for machine learning and artificial intelligence applications …

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

Deep neural networks (DNNs) have been a pivotal technology in a myriad of fields, boasting
remarkable achievements. Nevertheless, their substantial workload and inherent …

Although transfer learning has been employed successfully with pre-trained models based
on large convolutional neural networks, the demand for huge storage space makes it …

Convolutional neural networks (CNNs) have been successfully employed in remote sensing
image classification because of their robust feature representation for different visual tasks …

Neural network pruning is a critical field aimed at reducing the infrastructure costs of neural
networks by removing parameters. Traditional methods follow a fixed paradigm including …

Model compression methods are being developed to bridge the gap between the massive
scale of neural networks and the limited hardware resources on edge devices. Since most …

The integration of Internet of Things (IoT) in consumer environments enhances convenience
and security while increasing Human-Computer Interaction (HCI). However, this increased …

Recently, many studies have been carried out on model compression to handle the high
computational cost and high memory footprint brought by the implementation of deep neural …