11 citations · 63 across the 9 of their papers we have counts for
6 papers · 1 filter
An Information Theory-inspired Strategy for Automatic Network Pruning
Xiawu Zheng, Yuexiao Ma, Teng Xi +6
Despite superior performance on many computer vision tasks, deep convolution neural networks are well known to be compressed on devices that have resource constraints. Most existin…
Towards Compact CNNs via Collaborative Compression
Yuchao Li, Shaohui Lin, Jianzhuang Liu +7
Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in…
1xN Pattern for Pruning Convolutional Neural Networks
Mingbao Lin, Yuxin Zhang, Yuchao Li +6
Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as w…
Interpretable Neural Network Decoupling
Yuchao Li, Rongrong Ji, Shaohui Lin +5
The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the e…
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
Shaohui Lin, Rongrong Ji, Yuchao Li +2
The success of convolutional neural networks (CNNs) in computer vision applications has been accompanied by a significant increase of computation and memory costs, which prohibits…
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression
Yuchao Li, Shaohui Lin, Baochang Zhang +5
Compressing convolutional neural networks (CNNs) has received ever-increasing research focus. However, most existing CNN compression methods do not interpret their inherent structu…