290 citations · 944 across the 42 of their papers we have counts for
3 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…