32 citations · 46 across the 3 of their papers we have counts for
6 papers
Neural network compression via learnable wavelet transforms
Moritz Wolter, Shaohui Lin, Angela Yao
Wavelets are well known for data compression, yet have rarely been applied to the compression of neural networks. This paper shows how the fast wavelet transform can be used to com…
Training convolutional neural networks with cheap convolutions and online distillation
Jiao Xie, Shaohui Lin, Yichen Zhang +1
The large memory and computation consumption in convolutional neural networks (CNNs) has been one of the main barriers for deploying them on resource-limited systems. To this end,…
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 Optimal Structured CNN Pruning via Generative Adversarial Learning
Shaohui Lin, Rongrong Ji, Chenqian Yan +5
Structured pruning of filters or neurons has received increased focus for compressing convolutional neural networks. Most existing methods rely on multi-stage optimizations in a la…
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…