159 citations · 943 across the 39 of their papers we have counts for
5 papers · 1 filter
Re-parameterizing Your Optimizers rather than Architectures
Xiaohan Ding, Honghao Chen, Xiangyu Zhang +3
The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to train…
ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting
Xiaohan Ding, Tianxiang Hao, Jianchao Tan +4
We propose ResRep, a novel method for lossless channel pruning (a.k.a. filter pruning), which slims down a CNN by reducing the width (number of output channels) of convolutional la…
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…
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…