125 citations · 349 across the 12 of their papers we have counts for
4 papers · 1 filter
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Xiaohan Ding, Guiguang Ding, Xiangxin Zhou +3
Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices. DNN pruning i…
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
Xiaohan Ding, Yuchen Guo, Guiguang Ding +1
As designing appropriate Convolutional Neural Network (CNN) architecture in the context of a given application usually involves heavy human works or numerous GPU hours, the researc…
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
Xiaohan Ding, Guiguang Ding, Yuchen Guo +2
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architect…
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo +1
The redundancy is widely recognized in Convolutional Neural Networks (CNNs), which enables to remove unimportant filters from convolutional layers so as to slim the network with ac…