Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

Neural network pruning offers a promising prospect to facilitate deploying deep neural
networks on resource-limited devices. However, existing methods are still challenged by the …

Convolutional neural network (CNN) pruning has become one of the most successful
network compression approaches in recent years. Existing works on network pruning …

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 …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

In this paper, we analyze two popular network compression techniques, ie filter pruning and
low-rank decomposition, in a unified sense. By simply changing the way the sparsity …

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 …

Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the
key question that pruning algorithms target on. Many algorithms try to predict model …