The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

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

Channel pruning is among the predominant approaches to compress deep neural networks.
To this end, most existing pruning methods focus on selecting channels (filters) by …

Existing pruning techniques preserve deep neural networks' overall ability to make correct
predictions but could also amplify hidden biases during the compression process. We …

Most neural network pruning methods, such as filter-level and layer-level prunings, prune
the network model along one dimension (depth, width, or resolution) solely to meet a …

Artificial Intelligence (AI) has achieved significant advancements in technology and research
with the development over several decades, and is widely used in many areas including …

Channel pruning is a popular technique for compressing convolutional neural networks
(CNNs), where various pruning criteria have been proposed to remove the redundant filters …

This paper proposes an Information Bottleneck theory based filter pruning method that uses
a statistical measure called Mutual Information (MI). The MI between filters and class labels …

Filter pruning is one of the most effective methods to compress deep convolutional networks
(CNNs). In this paper, as a key component in filter pruning, We first propose a feature …

Abstract We proposed a Prior Gradient Mask Guided Pruning-aware Fine-Tuning (PGMPF)
framework to accelerate deep Convolutional Neural Networks (CNNs). In detail, the …