17 citations · 22 across the 5 of their papers we have counts for
5 papers
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks
Ping Xue, Yang Lu, Jingfei Chang +2
Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. Existing optimization method…
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks
Jingfei Chang, Yang Lu, Ping Xue +2
With the increase of structure complexity, convolutional neural networks (CNNs) take a fair amount of computation cost. Meanwhile, existing research reveals the salient parameter r…
Self-Distribution Binary Neural Networks
Ping Xue, Yang Lu, Jingfei Chang +2
In this work, we study the binary neural networks (BNNs) of which both the weights and activations are binary (i.e., 1-bit representation). Feature representation is critical for d…
ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN
Jingfei Chang, Yang Lu, Ping Xue +2
As the convolutional neural network (CNN) gets deeper and wider in recent years, the requirements for the amount of data and hardware resources have gradually increased. Meanwhile,…
UCP: Uniform Channel Pruning for Deep Convolutional Neural Networks Compression and Acceleration
Jingfei Chang, Yang Lu, Ping Xue +2
To apply deep CNNs to mobile terminals and portable devices, many scholars have recently worked on the compressing and accelerating deep convolutional neural networks. Based on thi…